diff --git a/.gitignore b/.gitignore
index 2e6fcf7..7e62ebb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -165,3 +165,5 @@ local_experiments/
 # Examples / training outputs
 examples/runs
 runs
+wandb
+videos
diff --git a/README.md b/README.md
index ef73fde..a4b33ac 100644
--- a/README.md
+++ b/README.md
@@ -1,32 +1,38 @@
 ![logo](./docs/_static/logo.png "Tetris Gymnasium")
 
-> Tetris Gymnasium is currently under early development!
-
 Tetris Gymnasium is tightly integrated with Gymnasium and exposes a simple API for training agents to play Tetris.
 
-The environment offers state-of-the-art performance and holds a high standard for code quality. With it, researchers and developers can focus on their research and development, rather than the environment itself.
-
 Getting started is easy. Here is a simple example of an environment with random actions:
 
 ```python
 import gymnasium as gym
 from tetris_gymnasium.envs import Tetris
 
-env = gym.make("tetris_gymnasium/Tetris")
-observation, info = env.reset(seed=42)
-for _ in range(1000):
-   action = env.action_space.sample()  # this is where you would insert your policy
-   observation, reward, terminated, truncated, info = env.step(action)
-
-   if terminated or truncated:
-      observation, info = env.reset()
+env = gym.make("tetris_gymnasium/Tetris", render_mode="ansi")
+env.reset(seed=42)
 
-env.close()
+terminated = False
+while not terminated:
+    print(env.render() + "\n")
+    action = env.action_space.sample()
+    observation, reward, terminated, truncated, info = env.step(action)
+print("Game Over!")
 ```
 
-##
+## Background
+
+Tetris Gymnasium tries to solve problems of other environments by being modular, understandable and adjustable. You can read more about the background in our paper: _Piece by Piece: Assembling a Modular Reinforcement Learning Environment for Tetris_ (SOON).
+
+Abstract:
 
-The documentation can be found on [GitHub Pages](https://max-we.github.io/Tetris-Gymnasium/)
+>The game of Tetris is an open challenge in machine learning and especially Reinforcement Learning (RL). Despite its popularity, contemporary environments for the game lack key qualities, such as a clear documentation, an up-to-date codebase or game related features.
+This work introduces Tetris Gymnasium, a modern RL environment built with Gymnasium, that aims to address these problems by being modular, understandable and adjustable.
+To evaluate Tetris Gymnasium on these qualities, a Deep Q Learning agent was trained and compared to a baseline environment, and it was found that it fulfills all requirements of a feature-complete RL environment while being adjustable to many different requirements.
+The source-code and documentation is available at on GitHub and can be used for free under the MIT license.
+
+## Documentation
+
+The full documentation of the project can be found on [GitHub Pages](https://max-we.github.io/Tetris-Gymnasium/).
 
 ## License
 
@@ -34,8 +40,4 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
 
 ## Acknowledgements
 
-We would like to thank the creators and maintainers of Gymnasium, CleanRL and Tetris-deep-Q-learning-pytorch for providing a powerful frameworks and reference implementations
-
----
-
-Enjoy using the Gymnasium Tetris Environment for your reinforcement learning experiments! If you have any questions or need further assistance, don't hesitate to reach out to us. Happy coding! 🎮🕹️
+We would like to thank the creators and maintainers of [Gymnasium](https://github.com/Farama-Foundation/Gymnasium), [CleanRL](https://github.com/vwxyzjn/cleanrl) and [Tetris-deep-Q-learning-pytorch](https://github.com/uvipen/Tetris-deep-Q-learning-pytorch) for providing a powerful frameworks and reference implementations.
diff --git a/docs/development/contributing.md b/docs/development/contributing.md
index 82e9de2..caab3b0 100644
--- a/docs/development/contributing.md
+++ b/docs/development/contributing.md
@@ -1,3 +1,54 @@
 # Contributing
 
-Under construction.
+Contributions are always welcome, no matter how large or small. Feel free to create an issue or a pull request.
+If you want to contribute, please read the following guidelines.
+
+The following section is based on the [Gymnasium contributing guidelines](https://github.com/Farama-Foundation/Gymnasium/blob/main/CONTRIBUTING.md).
+
+## Type checking
+
+The project uses `pyright` to check types.
+To type check locally, install `pyright` per official [instructions](https://github.com/microsoft/pyright#command-line).
+It's configuration lives within `pyproject.toml`. It includes list of included and excluded files currently supporting type checks.
+To run `pyright` for the project, run the pre-commit process (`pre-commit run --all-files`) or `pyright --project=pyproject.toml`
+Alternatively, pyright is a built-in feature of VSCode that will automatically provide type hinting.
+
+### Adding typing to more modules and packages
+
+If you would like to add typing to a module in the project,
+the list of included, excluded and strict files can be found in pyproject.toml (pyproject.toml -> [tool.pyright]).
+To run `pyright` for the project, run the pre-commit process (`pre-commit run --all-files`) or `pyright`
+
+## Git hooks
+
+The CI will run several checks on the new code pushed to the Gymnasium repository. These checks can also be run locally without waiting for the CI by following the steps below:
+
+1. [install `pre-commit`](https://pre-commit.com/#install),
+2. Install the Git hooks by running `pre-commit install`.
+
+Once those two steps are done, the Git hooks will be run automatically at every new commit.
+The Git hooks can also be run manually with `pre-commit run --all-files`, and if needed they can be skipped (not recommended) with `git commit --no-verify`.
+**Note:** you may have to run `pre-commit run --all-files` manually a couple of times to make it pass when you commit, as each formatting tool will first format the code and fail the first time but should pass the second time.
+
+Additionally, for pull requests, the project runs a number of tests for the whole project using [pytest](https://docs.pytest.org/en/latest/getting-started.html#install-pytest).
+These tests can be run locally with `pytest` in the root folder. If any doctest is modified, run `pytest --doctest-modules --doctest-continue-on-failure gymnasium` to check the changes.
+
+## Docstrings
+
+Pydocstyle has been added to the pre-commit process such that all new functions follow the [google docstring style](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html).
+All new functions require either a short docstring, a single line explaining the purpose of a function
+or a multiline docstring that documents each argument and the return type (if there is one) of the function.
+In addition, new file and class require top docstrings that should outline the purpose of the file/class.
+For classes, code block examples can be provided in the top docstring and not the constructor arguments.
+
+To check your docstrings are correct, run `pre-commit run --all-files` or `pydocstyle --source --explain --convention=google`.
+If all docstrings that fail, the source and reason for the failure is provided.
+
+## Building the docs
+
+You can use `sphinx-autobuild` to build the documentation locally.
+
+```shell
+cd docs
+poetry run sphinx-autobuild -b dirhtml --watch ../tetris_gymnasium --re-ignore "pickle$" . _build
+```
diff --git a/docs/index.md b/docs/index.md
index 0c84d10..f99023e 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -11,29 +11,50 @@ lastpage:
 ```{project-heading}
 A customisable, easy-to-use and performant Tetris environment for Gymnasium
 ```
-
 Tetris Gymnasium is tightly integrated with Gymnasium and exposes a simple API for training agents to play Tetris.
 
-The environment offers state-of-the-art performance and holds a high standard for code quality. With it, researchers and developers can focus on their research and development, rather than the environment itself.
-
 Getting started is easy. Here is a simple example of an environment with random actions:
 
 ```{code-block} python
 import gymnasium as gym
 from tetris_gymnasium.envs import Tetris
 
-env = gym.make("tetris_gymnasium/Tetris")
-observation, info = env.reset(seed=42)
-for _ in range(1000):
-   action = env.action_space.sample()  # this is where you would insert your policy
-   observation, reward, terminated, truncated, info = env.step(action)
+env = gym.make("tetris_gymnasium/Tetris", render_mode="ansi")
+env.reset(seed=42)
 
-   if terminated or truncated:
-      observation, info = env.reset()
-
-env.close()
+terminated = False
+while not terminated:
+    print(env.render() + "\n")
+    action = env.action_space.sample()
+    observation, reward, terminated, truncated, info = env.step(action)
+print("Game Over!")
 ```
 
+# Information
+
+## Background
+
+Tetris Gymnasium tries to solve problems of other environments by being modular, understandable and adjustable. You can read more about the background in our paper: _Piece by Piece: Assembling a Modular Reinforcement Learning Environment for Tetris_ (SOON).
+
+Abstract:
+
+>The game of Tetris is an open challenge in machine learning and especially Reinforcement Learning (RL). Despite its popularity, contemporary environments for the game lack key qualities, such as a clear documentation, an up-to-date codebase or game related features.
+This work introduces Tetris Gymnasium, a modern RL environment built with Gymnasium, that aims to address these problems by being modular, understandable and adjustable.
+To evaluate Tetris Gymnasium on these qualities, a Deep Q Learning agent was trained and compared to a baseline environment, and it was found that it fulfills all requirements of a feature-complete RL environment while being adjustable to many different requirements.
+The source-code and documentation is available at on GitHub and can be used for free under the MIT license.
+
+## Documentation
+
+The full documentation of the project can be found on [GitHub Pages](https://max-we.github.io/Tetris-Gymnasium/).
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](../LICENSE) file for details.
+
+## Acknowledgements
+
+We would like to thank the creators and maintainers of [Gymnasium](https://github.com/Farama-Foundation/Gymnasium), [CleanRL](https://github.com/vwxyzjn/cleanrl) and [Tetris-deep-Q-learning-pytorch](https://github.com/uvipen/Tetris-deep-Q-learning-pytorch) for providing a powerful frameworks and reference implementations.
+
 ```{toctree}
 :maxdepth: 2
 :caption: Introduction
diff --git a/docs/introduction/installation.md b/docs/introduction/installation.md
index 8db0d77..7c9635a 100644
--- a/docs/introduction/installation.md
+++ b/docs/introduction/installation.md
@@ -1,11 +1,11 @@
 # Installation
 
-At the moment, the only way to install the package is to clone the repository and to install it using poetry.
+At the moment, you can install the environment by cloning the repository and running `poetry install`.
+
+> In the near future, this library will be distributed via PyPI.
 
 ```{code-block} bash
 git clone https://github.com/Max-We/Tetris-Gymnasium.git
 cd Tetris-Gymnasium
 poetry install
 ```
-
-In the near future, this library will be distributed via PyPI.
diff --git a/docs/introduction/quickstart.md b/docs/introduction/quickstart.md
index af7bb69..ec3dc34 100644
--- a/docs/introduction/quickstart.md
+++ b/docs/introduction/quickstart.md
@@ -43,3 +43,5 @@ poetry run python examples/train_lin.py # uses a linear model
 # or
 poetry run python examples/train_cnn.py # uses convolutions
 ```
+
+You can refer to the [CleanRL documentation](https://docs.cleanrl.dev/rl-algorithms/dqn/) for more information on the training script. Note: If you have tracking enabled, you will be prompted to login to Weights & Biases during the first run. This behavior can be adjusted in the script.
diff --git a/docs/utilities/wrappers.md b/docs/utilities/wrappers.md
index 06aff8d..bd421a1 100644
--- a/docs/utilities/wrappers.md
+++ b/docs/utilities/wrappers.md
@@ -12,11 +12,11 @@ shape of the observation space or for adding additional information to the obser
 ### Implementations
 
 ```{eval-rst}
-.. autoclass:: tetris_gymnasium.wrappers.observation.CnnObservation
+.. autoclass:: tetris_gymnasium.wrappers.observation.RgbObservation
 ```
 
 #### Methods
 ```{eval-rst}
-.. automethod:: tetris_gymnasium.wrappers.observation.CnnObservation.observation
-.. automethod:: tetris_gymnasium.wrappers.observation.CnnObservation.render
+.. automethod:: tetris_gymnasium.wrappers.observation.RgbObservation.observation
+.. automethod:: tetris_gymnasium.wrappers.observation.RgbObservation.render
 ```
diff --git a/examples/play_interactive.py b/examples/play_interactive.py
index 94bddc4..e487f9a 100644
--- a/examples/play_interactive.py
+++ b/examples/play_interactive.py
@@ -7,14 +7,14 @@
 
 if __name__ == "__main__":
     # Create an instance of Tetris
-    tetris_game = gym.make("tetris_gymnasium/Tetris", render_mode="human")
-    tetris_game.reset(seed=42)
+    env = gym.make("tetris_gymnasium/Tetris", render_mode="human")
+    env.reset(seed=42)
 
     # Main game loop
     terminated = False
     while not terminated:
         # Render the current state of the game as text
-        tetris_game.render()
+        env.render()
 
         # Pick an action from user input mapped to the keyboard
         action = None
@@ -22,33 +22,31 @@
             key = cv2.waitKey(1)
 
             if key == ord("a"):
-                action = tetris_game.unwrapped.actions.move_left
+                action = env.unwrapped.actions.move_left
             elif key == ord("d"):
-                action = tetris_game.unwrapped.actions.move_right
+                action = env.unwrapped.actions.move_right
             elif key == ord("s"):
-                action = tetris_game.unwrapped.actions.move_down
+                action = env.unwrapped.actions.move_down
             elif key == ord("w"):
-                action = tetris_game.unwrapped.actions.rotate_counterclockwise
+                action = env.unwrapped.actions.rotate_counterclockwise
             elif key == ord("e"):
-                action = tetris_game.unwrapped.actions.rotate_clockwise
+                action = env.unwrapped.actions.rotate_clockwise
             elif key == ord(" "):
-                action = tetris_game.unwrapped.actions.hard_drop
+                action = env.unwrapped.actions.hard_drop
             elif key == ord("q"):
-                action = tetris_game.unwrapped.actions.swap
+                action = env.unwrapped.actions.swap
             elif key == ord("r"):
-                tetris_game.reset(seed=42)
+                env.reset(seed=42)
                 break
 
             if (
-                cv2.getWindowProperty(
-                    tetris_game.unwrapped.window_name, cv2.WND_PROP_VISIBLE
-                )
+                cv2.getWindowProperty(env.unwrapped.window_name, cv2.WND_PROP_VISIBLE)
                 == 0
             ):
                 sys.exit()
 
         # Perform the action
-        observation, reward, terminated, truncated, info = tetris_game.step(action)
+        observation, reward, terminated, truncated, info = env.step(action)
 
     # Game over
     print("Game Over!")
diff --git a/examples/play_interactive_cnn.py b/examples/play_interactive_cnn.py
index 0bb80a4..f6bc511 100644
--- a/examples/play_interactive_cnn.py
+++ b/examples/play_interactive_cnn.py
@@ -4,19 +4,19 @@
 import gymnasium as gym
 
 from tetris_gymnasium.envs import Tetris
-from tetris_gymnasium.wrappers.observation import CnnObservation
+from tetris_gymnasium.wrappers.observation import RgbObservation
 
 if __name__ == "__main__":
     # Create an instance of Tetris
-    tetris_game = gym.make("tetris_gymnasium/Tetris", render_mode="human")
-    tetris_game.reset(seed=42)
-    tetris_game = CnnObservation(tetris_game)
+    env = gym.make("tetris_gymnasium/Tetris", render_mode="human")
+    env = RgbObservation(env)
+    env.reset(seed=42)
 
     # Main game loop
     terminated = False
     while not terminated:
         # Render the current state of the game as text
-        tetris_game.render()
+        env.render()
 
         # Pick an action from user input mapped to the keyboard
         action = None
@@ -24,33 +24,31 @@
             key = cv2.waitKey(1)
 
             if key == ord("a"):
-                action = tetris_game.unwrapped.actions.move_left
+                action = env.unwrapped.actions.move_left
             elif key == ord("d"):
-                action = tetris_game.unwrapped.actions.move_right
+                action = env.unwrapped.actions.move_right
             elif key == ord("s"):
-                action = tetris_game.unwrapped.actions.move_down
+                action = env.unwrapped.actions.move_down
             elif key == ord("w"):
-                action = tetris_game.unwrapped.actions.rotate_counterclockwise
+                action = env.unwrapped.actions.rotate_counterclockwise
             elif key == ord("e"):
-                action = tetris_game.unwrapped.actions.rotate_clockwise
+                action = env.unwrapped.actions.rotate_clockwise
             elif key == ord(" "):
-                action = tetris_game.unwrapped.actions.hard_drop
+                action = env.unwrapped.actions.hard_drop
             elif key == ord("q"):
-                action = tetris_game.unwrapped.actions.swap
+                action = env.unwrapped.actions.swap
             elif key == ord("r"):
-                tetris_game.reset(seed=42)
+                env.reset(seed=42)
                 break
 
             if (
-                cv2.getWindowProperty(
-                    tetris_game.unwrapped.window_name, cv2.WND_PROP_VISIBLE
-                )
+                cv2.getWindowProperty(env.unwrapped.window_name, cv2.WND_PROP_VISIBLE)
                 == 0
             ):
                 sys.exit()
 
         # Perform the action
-        observation, reward, terminated, truncated, info = tetris_game.step(action)
+        observation, reward, terminated, truncated, info = env.step(action)
 
     # Game over
     print("Game Over!")
diff --git a/examples/play_random.py b/examples/play_random.py
index 2f9c88e..94ca2e4 100644
--- a/examples/play_random.py
+++ b/examples/play_random.py
@@ -3,22 +3,12 @@
 from tetris_gymnasium.envs.tetris import Tetris
 
 if __name__ == "__main__":
-    # Create an instance of Tetris
-    tetris_game = gym.make("tetris_gymnasium/Tetris", render_mode="ansi")
-    tetris_game.reset(seed=42)
+    env = gym.make("tetris_gymnasium/Tetris", render_mode="ansi")
+    env.reset(seed=42)
 
-    # Main game loop
     terminated = False
     while not terminated:
-        # Render the current state of the game as text
-        ansi = tetris_game.render()
-        print(ansi + "\n")
-
-        # Take a random action (for demonstration purposes)
-        action = tetris_game.action_space.sample()
-
-        # Perform the action
-        observation, reward, terminated, truncated, info = tetris_game.step(action)
-
-    # Game over
+        print(env.render() + "\n")
+        action = env.action_space.sample()
+        observation, reward, terminated, truncated, info = env.step(action)
     print("Game Over!")
diff --git a/examples/train_cnn.py b/examples/train_cnn.py
index a96cf2d..566a66c 100644
--- a/examples/train_cnn.py
+++ b/examples/train_cnn.py
@@ -17,10 +17,12 @@
 import torch.nn.functional as F
 import torch.optim as optim
 import tyro
+from stable_baselines3.common.atari_wrappers import ClipRewardEnv
 from stable_baselines3.common.buffers import ReplayBuffer
 from torch.utils.tensorboard import SummaryWriter
 
 from tetris_gymnasium.envs import Tetris
+from tetris_gymnasium.wrappers.observation import RgbObservation
 
 
 # Evaluation
@@ -75,13 +77,13 @@ class Args:
     """if toggled, `torch.backends.cudnn.deterministic=False`"""
     cuda: bool = True
     """if toggled, cuda will be enabled by default"""
-    track: bool = False
+    track: bool = True
     """if toggled, this experiment will be tracked with Weights and Biases"""
-    wandb_project_name: str = "cleanRL"
+    wandb_project_name: str = "tetris_gymnasium"
     """the wandb's project name"""
     wandb_entity: str = None
     """the entity (team) of wandb's project"""
-    capture_video: bool = False
+    capture_video: bool = True
     """whether to capture videos of the agent performances (check out `videos` folder)"""
     save_model: bool = False
     """whether to save model into the `runs/{run_name}` folder"""
@@ -94,7 +96,7 @@ class Args:
     # env_id: str = "BreakoutNoFrameskip-v4"
     env_id: str = "tetris_gymnasium/Tetris"
     """the id of the environment"""
-    total_timesteps: int = 10000000
+    total_timesteps: int = 2000000
     """total timesteps of the experiments"""
     learning_rate: float = 1e-4
     """the learning rate of the optimizer"""
@@ -126,12 +128,18 @@ def make_env(env_id, seed, idx, capture_video, run_name):
     def thunk():
         if capture_video and idx == 0:
             env = gym.make(env_id, render_mode="rgb_array")
+            env = RgbObservation(env)
             env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
         else:
             env = gym.make(env_id)
         env = gym.wrappers.RecordEpisodeStatistics(env)
         env.action_space.seed(seed)
 
+        env = ClipRewardEnv(env)
+
+        env = gym.wrappers.ResizeObservation(env, (84, 84))
+        env = gym.wrappers.GrayScaleObservation(env)
+
         env = gym.wrappers.FrameStack(env, 4)
 
         return env
@@ -144,20 +152,20 @@ class QNetwork(nn.Module):
     def __init__(self, env):
         super().__init__()
         self.network = nn.Sequential(
-            nn.Conv2d(4, 32, 3, stride=1),
+            nn.Conv2d(4, 32, 8, stride=4),
             nn.ReLU(),
             nn.Conv2d(32, 64, 4, stride=2),
             nn.ReLU(),
             nn.Conv2d(64, 64, 3, stride=1),
             nn.ReLU(),
             nn.Flatten(),
-            nn.Linear(384, 512),
+            nn.Linear(3136, 512),
             nn.ReLU(),
             nn.Linear(512, env.single_action_space.n),
         )
 
     def forward(self, x):
-        return self.network(x)
+        return self.network(x / 255.0)
 
 
 def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
@@ -247,9 +255,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
         else:
             # Normalization by dividing with piece count
             # Todo: Create api to get how many pieces are in env / do normalize
-            q_values = q_network(
-                torch.Tensor(obs).to(device) / envs.observation_space.high
-            )
+            q_values = q_network(torch.Tensor(obs).to(device))
             actions = torch.argmax(q_values, dim=1).cpu().numpy()
 
         # TRY NOT TO MODIFY: execute the game and log data.
@@ -260,7 +266,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             for info in infos["final_info"]:
                 if info and "episode" in info:
                     print(
-                        f"global_step={global_step}, episodic_return={info['episode']['r']}"
+                        f"global_step={global_step}, episodic_return={info['episode']['r']}, episodic_len={info['episode']['l']}"
                     )
                     writer.add_scalar(
                         "charts/episodic_return", info["episode"]["r"], global_step
diff --git a/examples/train_lin.py b/examples/train_lin.py
index 9e533f9..b8d0fcd 100644
--- a/examples/train_lin.py
+++ b/examples/train_lin.py
@@ -76,9 +76,9 @@ class Args:
     """if toggled, `torch.backends.cudnn.deterministic=False`"""
     cuda: bool = True
     """if toggled, cuda will be enabled by default"""
-    track: bool = False
+    track: bool = True
     """if toggled, this experiment will be tracked with Weights and Biases"""
-    wandb_project_name: str = "cleanRL"
+    wandb_project_name: str = "tetris_gymnasium"
     """the wandb's project name"""
     wandb_entity: str = None
     """the entity (team) of wandb's project"""
@@ -241,12 +241,7 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
                 [envs.single_action_space.sample() for _ in range(envs.num_envs)]
             )
         else:
-            # Normalization by dividing with piece count
-            # Todo: Create api to get how many pieces are in env / do normalize
-            print(obs.shape)
-            q_values = q_network(
-                torch.Tensor(obs).to(device) / envs.observation_space.high
-            )
+            q_values = q_network(torch.Tensor(obs).to(device))
             actions = torch.argmax(q_values, dim=1).cpu().numpy()
 
         # TRY NOT TO MODIFY: execute the game and log data.
diff --git a/poetry.lock b/poetry.lock
index f0c0c1d..3f1a33f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -13,18 +13,22 @@ files = [
 
 [[package]]
 name = "accessible-pygments"
-version = "0.0.4"
+version = "0.0.5"
 description = "A collection of accessible pygments styles"
 optional = false
-python-versions = "*"
+python-versions = ">=3.9"
 files = [
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+tests = ["hypothesis", "pytest"]
+
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+optional = false
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+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
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+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
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+version = "2.15.0"
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 ]
 
 [package.dependencies]
-numpy = ">=1.20,<2.0"
+numpy = ">=1.20"
 
 [package.extras]
 bokeh = ["bokeh", "selenium"]
 docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
-mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.6.1)", "types-Pillow"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pillow"]
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
@@ -352,6 +378,17 @@ files = [
 docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
 tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
+[[package]]
+name = "decorator"
+version = "4.4.2"
+description = "Decorators for Humans"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*"
+files = [
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+]
+
 [[package]]
 name = "distlib"
 version = "0.3.8"
@@ -363,6 +400,20 @@ files = [
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 ]
 
+[[package]]
+name = "docker-pycreds"
+version = "0.4.0"
+description = "Python bindings for the docker credentials store API"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+six = ">=1.4.0"
+
 [[package]]
 name = "docstring-parser"
 version = "0.16"
@@ -385,6 +436,20 @@ files = [
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 ]
 
+[[package]]
+name = "exceptiongroup"
+version = "1.2.1"
+description = "Backport of PEP 654 (exception groups)"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+test = ["pytest (>=6)"]
+
 [[package]]
 name = "farama-notifications"
 version = "0.0.4"
@@ -398,69 +463,69 @@ files = [
 
 [[package]]
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+version = "3.14.0"
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 typing = ["typing-extensions (>=4.8)"]
 
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-protobuf = ["grpcio-tools (>=1.62.1)"]
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 [[package]]
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 testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
 toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
 
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 [[package]]
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 ]
 
 [package.extras]
@@ -647,15 +747,65 @@ license = ["ukkonen"]
 
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+optional = false
+python-versions = ">=3.8"
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+
+[package.extras]
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+all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+build = ["wheel"]
+dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
+fits = ["astropy"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
+gdal = ["gdal"]
+itk = ["itk"]
+linting = ["black", "flake8"]
+pillow-heif = ["pillow-heif"]
+pyav = ["av"]
+test = ["fsspec[github]", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
+
+[[package]]
+name = "imageio-ffmpeg"
+version = "0.4.9"
+description = "FFMPEG wrapper for Python"
+optional = false
+python-versions = ">=3.5"
+files = [
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+]
+
+[package.dependencies]
+setuptools = "*"
+
 [[package]]
 name = "imagesize"
 version = "1.4.1"
@@ -669,13 +819,13 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "7.0.2"
+version = "7.1.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -684,17 +834,17 @@ zipp = ">=0.5"
 [package.extras]
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+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
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+version = "6.4.0"
 description = "Read resources from Python packages"
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 python-versions = ">=3.8"
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+
+[[package]]
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+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
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-[package.dependencies]
-six = "*"
-tornado = {version = "*", markers = "python_version > \"2.7\""}
-
 [[package]]
 name = "markdown"
 version = "3.6"
@@ -962,39 +1111,39 @@ files = [
 
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 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.3.1"
-numpy = ">=1.21,<2"
+numpy = ">=1.21"
 packaging = ">=20.0"
 pillow = ">=8"
 pyparsing = ">=2.3.1"
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+description = "Intel® oneAPI Math Kernel Library"
+optional = false
+python-versions = "*"
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+tbb = "==2021.*"
+
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+tqdm = ">=4.11.2,<5.0"
+
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+
 [[package]]
 name = "mpmath"
 version = "1.3.0"
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-version = "2.19.3"
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 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
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 python-versions = ">=3"
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 [package.dependencies]
-numpy = {version = ">=1.22.4,<2", markers = "python_version < \"3.11\""}
+numpy = [
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+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
+]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
 tzdata = ">=2022.7"
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 [package.dependencies]
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 pyyaml = ">=5.1"
 virtualenv = ">=20.10.0"
 
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 ]
 
+[package.extras]
+test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+
 [[package]]
 name = "pydata-sphinx-theme"
 version = "0.15.2"
@@ -1587,17 +1826,16 @@ toml = ["tomli (>=1.2.3)"]
 
 [[package]]
 name = "pygments"
-version = "2.17.2"
+version = "2.18.0"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-plugins = ["importlib-metadata"]
 windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
@@ -1616,13 +1854,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pyright"
-version = "1.1.355"
+version = "1.1.362"
 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1756,20 +1994,170 @@ pygments = ">=2.13.0,<3.0.0"
 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
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+python-versions = ">=3.6"
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+
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+
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+anthropic = ["anthropic (>=0.16)"]
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+asyncpg = ["asyncpg (>=0.23)"]
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+celery-redbeat = ["celery-redbeat (>=2)"]
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 [[package]]
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-version = "69.2.0"
+version = "69.5.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
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+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
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 [[package]]
@@ -1797,6 +2185,28 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "smmap"
+version = "5.0.1"
+description = "A pure Python implementation of a sliding window memory map manager"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"},
+    {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
+]
+
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
+]
+
 [[package]]
 name = "snowballstemmer"
 version = "2.2.0"
@@ -1821,20 +2231,20 @@ files = [
 
 [[package]]
 name = "sphinx"
-version = "7.2.6"
+version = "7.3.7"
 description = "Python documentation generator"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinx-7.2.6-py3-none-any.whl", hash = "sha256:1e09160a40b956dc623c910118fa636da93bd3ca0b9876a7b3df90f07d691560"},
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+    {file = "sphinx-7.3.7-py3-none-any.whl", hash = "sha256:413f75440be4cacf328f580b4274ada4565fb2187d696a84970c23f77b64d8c3"},
+    {file = "sphinx-7.3.7.tar.gz", hash = "sha256:a4a7db75ed37531c05002d56ed6948d4c42f473a36f46e1382b0bd76ca9627bc"},
 ]
 
 [package.dependencies]
-alabaster = ">=0.7,<0.8"
+alabaster = ">=0.7.14,<0.8.0"
 babel = ">=2.9"
 colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
-docutils = ">=0.18.1,<0.21"
+docutils = ">=0.18.1,<0.22"
 imagesize = ">=1.3"
 importlib-metadata = {version = ">=4.8", markers = "python_version < \"3.10\""}
 Jinja2 = ">=3.0"
@@ -1848,30 +2258,34 @@ sphinxcontrib-htmlhelp = ">=2.0.0"
 sphinxcontrib-jsmath = "*"
 sphinxcontrib-qthelp = "*"
 sphinxcontrib-serializinghtml = ">=1.1.9"
+tomli = {version = ">=2", markers = "python_version < \"3.11\""}
 
 [package.extras]
 docs = ["sphinxcontrib-websupport"]
-lint = ["docutils-stubs", "flake8 (>=3.5.0)", "flake8-simplify", "isort", "mypy (>=0.990)", "ruff", "sphinx-lint", "types-requests"]
-test = ["cython (>=3.0)", "filelock", "html5lib", "pytest (>=4.6)", "setuptools (>=67.0)"]
+lint = ["flake8 (>=3.5.0)", "importlib_metadata", "mypy (==1.9.0)", "pytest (>=6.0)", "ruff (==0.3.7)", "sphinx-lint", "tomli", "types-docutils", "types-requests"]
+test = ["cython (>=3.0)", "defusedxml (>=0.7.1)", "pytest (>=6.0)", "setuptools (>=67.0)"]
 
 [[package]]
 name = "sphinx-autobuild"
-version = "2024.2.4"
-description = "Rebuild Sphinx documentation on changes, with live-reload in the browser."
+version = "2024.4.16"
+description = "Rebuild Sphinx documentation on changes, with hot reloading in the browser."
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "sphinx_autobuild-2024.4.16-py3-none-any.whl", hash = "sha256:f2522779d30fcbf0253e09714f274ce8c608cb6ebcd67922b1c54de59faba702"},
+    {file = "sphinx_autobuild-2024.4.16.tar.gz", hash = "sha256:1c0ed37a1970eed197f9c5a66d65759e7c4e4cba7b5a5d77940752bf1a59f2c7"},
 ]
 
 [package.dependencies]
 colorama = "*"
-livereload = "*"
 sphinx = "*"
+starlette = ">=0.35"
+uvicorn = ">=0.25"
+watchfiles = ">=0.20"
+websockets = ">=11"
 
 [package.extras]
-test = ["pytest (>=6.0)", "pytest-cov"]
+test = ["pytest (>=6)"]
 
 [[package]]
 name = "sphinx-basic-ng"
@@ -1986,13 +2400,13 @@ test = ["pytest"]
 
 [[package]]
 name = "stable-baselines3"
-version = "2.2.1"
+version = "2.3.2"
 description = "Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "stable_baselines3-2.2.1.tar.gz", hash = "sha256:7db00bea41c82448d9243529059a9bc8131c0fe751098bd05e3d8822d2978a4d"},
+    {file = "stable_baselines3-2.3.2-py3-none-any.whl", hash = "sha256:a1cafff6ec23bd4e8f4f20dd829008dc1af58ec2d5d8fc0cd0929b4b4544c9b2"},
+    {file = "stable_baselines3-2.3.2.tar.gz", hash = "sha256:2f8188916e607571c4c24f88a9ff6f84edafb2cf22d5d24f9c199563c12ff168"},
 ]
 
 [package.dependencies]
@@ -2007,7 +2421,25 @@ torch = ">=1.13"
 docs = ["sphinx (>=5,<8)", "sphinx-autobuild", "sphinx-copybutton", "sphinx-rtd-theme (>=1.3.0)", "sphinxcontrib.spelling"]
 extra = ["autorom[accept-rom-license] (>=0.6.1,<0.7.0)", "opencv-python", "pillow", "psutil", "pygame", "rich", "shimmy[atari] (>=1.3.0,<1.4.0)", "tensorboard (>=2.9.1)", "tqdm"]
 extra-no-roms = ["opencv-python", "pillow", "psutil", "pygame", "rich", "shimmy[atari] (>=1.3.0,<1.4.0)", "tensorboard (>=2.9.1)", "tqdm"]
-tests = ["black (>=23.9.1,<24)", "mypy", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.288)"]
+tests = ["black (>=24.2.0,<25)", "mypy", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.3.1)"]
+
+[[package]]
+name = "starlette"
+version = "0.37.2"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "starlette-0.37.2.tar.gz", hash = "sha256:9af890290133b79fc3db55474ade20f6220a364a0402e0b556e7cd5e1e093823"},
+]
+
+[package.dependencies]
+anyio = ">=3.4.0,<5"
+typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
 
 [[package]]
 name = "sympy"
@@ -2023,6 +2455,19 @@ files = [
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "tbb"
+version = "2021.12.0"
+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
+optional = false
+python-versions = "*"
+files = [
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+    {file = "tbb-2021.12.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:a925e9a7c77d3a46ae31c34b0bb7f801c4118e857d137b68f68a8e458fcf2bd7"},
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+    {file = "tbb-2021.12.0-py3-none-win_amd64.whl", hash = "sha256:fc2772d850229f2f3df85f1109c4844c495a2db7433d38200959ee9265b34789"},
+]
+
 [[package]]
 name = "tensorboard"
 version = "2.16.2"
@@ -2069,42 +2514,38 @@ files = [
 
 [[package]]
 name = "torch"
-version = "2.2.1"
+version = "2.3.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = "*"
 jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
 networkx = "*"
 nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
@@ -2115,10 +2556,10 @@ nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linu
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-nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
+triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -2126,38 +2567,38 @@ opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.9.1)"]
 
 [[package]]
-name = "tornado"
-version = "6.4"
-description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+name = "tqdm"
+version = "4.66.4"
+description = "Fast, Extensible Progress Meter"
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">=3.7"
 files = [
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 ]
 
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "triton"
-version = "2.2.0"
+version = "2.3.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 files = [
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+    {file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"},
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 ]
 
 [package.dependencies]
@@ -2170,13 +2611,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
 [[package]]
 name = "typer"
-version = "0.9.0"
+version = "0.9.4"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "typer-0.9.4.tar.gz", hash = "sha256:f714c2d90afae3a7929fcd72a3abb08df305e1ff61719381384211c4070af57f"},
 ]
 
 [package.dependencies]
@@ -2187,17 +2628,17 @@ typing-extensions = ">=3.7.4.3"
 all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
 dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
 doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
-test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
+test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.971)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
 [[package]]
@@ -2249,15 +2690,34 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
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+
 [[package]]
 name = "virtualenv"
-version = "20.25.1"
+version = "20.26.1"
 description = "Virtual Python Environment builder"
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 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -2266,18 +2726,232 @@ filelock = ">=3.12.2,<4"
 platformdirs = ">=3.9.1,<5"
 
 [package.extras]
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+
 [[package]]
 name = "werkzeug"
-version = "3.0.1"
+version = "3.0.3"
 description = "The comprehensive WSGI web application library."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2303,5 +2977,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.9,<3.11"
-content-hash = "b8b6b5b51eea5aa7925f41c6e84d5ae38db572283297db5a07198a14d088a869"
+python-versions = ">=3.9,<4.0"
+content-hash = "b5c2eac2b193f764d14b19e2b3bce9828c52b6ed9bbd12d7bdf1be6ceea551c0"
diff --git a/pyproject.toml b/pyproject.toml
index ba59161..9aa8e86 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["mw <maximilian.weichart@icloud.com>"]
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.9,<3.11"
+python = ">=3.9,<4.0"
 gymnasium = ">=0.28.1"
 numpy = "^1.21.6"
 
@@ -22,6 +22,8 @@ typer = "^0.9.0"
 stable-baselines3 = "^2.2.1"
 tyro = "^0.7.3"
 tensorboard = "^2.10.0"
+wandb = "^0.17.0"
+moviepy = "^1.0.3"
 
 [tool.poetry.group.docs.dependencies]
 sphinx = "^7"
diff --git a/tetris_gymnasium/envs/__init__.py b/tetris_gymnasium/envs/__init__.py
index 4f5f88b..9b42edb 100644
--- a/tetris_gymnasium/envs/__init__.py
+++ b/tetris_gymnasium/envs/__init__.py
@@ -10,5 +10,4 @@
 register(
     id="tetris_gymnasium/Tetris",
     entry_point="tetris_gymnasium.envs:Tetris",
-    max_episode_steps=10,
 )
diff --git a/tetris_gymnasium/envs/tetris.py b/tetris_gymnasium/envs/tetris.py
index 5fb68b8..db40911 100644
--- a/tetris_gymnasium/envs/tetris.py
+++ b/tetris_gymnasium/envs/tetris.py
@@ -63,6 +63,7 @@ def __init__(
         render_mode=None,
         width=10,
         height=20,
+        gravity=True,
         randomizer: Randomizer = BagRandomizer,
         holder: TetrominoHolder = TetrominoHolder,
         queue: TetrominoQueue = TetrominoQueue,
@@ -77,6 +78,7 @@ def __init__(
             render_mode: The mode to use for rendering. If None, no rendering will be done.
             width: The width of the board.
             height: The height of the board.
+            gravity: Whether gravity is enabled in the game.
             randomizer: The :class:`Randomizer` to use for selecting tetrominoes.
             holder: The :class:`TetrominoHolder` to use for storing tetrominoes.
             queue: The :class:`TetrominoQueue` to use for holding tetrominoes temporarily.
@@ -116,6 +118,7 @@ def __init__(
         self.queue = queue(randomizer(len(tetrominoes)), 5)
         self.holder = holder()
         self.has_swapped = False
+        self.gravity_enabled = gravity
 
         # Position
         self.x: int = 0
@@ -183,7 +186,7 @@ def step(self, action: ActType) -> "tuple[dict, float, bool, bool, dict]":
 
         game_over = False
         truncated = False  # Tetris without levels will never truncate
-        reward = self.rewards.alife
+        reward = 0
 
         if action == self.actions.move_left:
             if not self.collision(self.active_tetromino, self.x - 1, self.y):
@@ -216,18 +219,15 @@ def step(self, action: ActType) -> "tuple[dict, float, bool, bool, dict]":
                 else:
                     self.reset_tetromino_position()
         elif action == self.actions.hard_drop:
-            # 1. Drop the tetromino and lock it in place
-            self.drop_active_tetromino()
-            self.place_active_tetromino()
-            reward += self.score(self.clear_filled_rows())
+            reward, game_over = self.commit_active_tetromino()
 
-            # 2. Spawn the next tetromino and check if the game continues
-            game_over = not self.spawn_tetromino()
-            if game_over:
-                reward = self.rewards.game_over
-
-            # 3. Reset the swap flag (agent can swap once per tetromino)
-            self.has_swapped = False
+        # Gravity
+        if self.gravity_enabled and action != self.actions.hard_drop:
+            if not self.collision(self.active_tetromino, self.x, self.y + 1):
+                self.y += 1
+            else:
+                # If there's no more room to move, lock in the tetromino
+                reward, game_over = self.commit_active_tetromino()
 
         return self._get_obs(), reward, game_over, truncated, self._get_info()
 
@@ -373,6 +373,31 @@ def drop_active_tetromino(self):
         while not self.collision(self.active_tetromino, self.x, self.y + 1):
             self.y += 1
 
+    def commit_active_tetromino(self):
+        """Commit the active tetromino to the board.
+
+        After locking in the tetromino, the game checks if any rows are filled and clears them.
+        Finally, it spawns the next tetromino.
+
+        Returns
+            The reward for the current step and whether the game is over.
+        """
+        # 1. Drop the tetromino and lock it in place
+        self.drop_active_tetromino()
+        self.place_active_tetromino()
+        reward = self.score(self.clear_filled_rows())
+
+        # 2. Spawn the next tetromino and check if the game continues
+        game_over = not self.spawn_tetromino()
+        reward += self.rewards.alife
+        if game_over:
+            reward = self.rewards.game_over
+
+        # 3. Reset the swap flag (agent can swap once per tetromino)
+        self.has_swapped = False
+
+        return reward, game_over
+
     def clear_filled_rows(self) -> int:
         """Clear any filled rows on the board.
 
@@ -444,6 +469,9 @@ def project_active_tetromino(self):
         on the board to render it.
         """
         projection = self.board.copy()
+        if self.collision(self.active_tetromino, self.x, self.y):
+            return projection
+
         slices = self.get_tetromino_slices(self.active_tetromino, self.x, self.y)
         projection[slices] += self.active_tetromino.matrix
         return projection
diff --git a/tetris_gymnasium/mappings/rewards.py b/tetris_gymnasium/mappings/rewards.py
index d899bac..e24e581 100644
--- a/tetris_gymnasium/mappings/rewards.py
+++ b/tetris_gymnasium/mappings/rewards.py
@@ -9,6 +9,6 @@ class RewardsMapping:
     The mapping can be extended to include additional rewards.
     """
 
-    alife: float = 0.001
+    alife: float = 0.01
     clear_line: float = 1
-    game_over: float = -2
+    game_over: float = 0
diff --git a/tetris_gymnasium/wrappers/observation.py b/tetris_gymnasium/wrappers/observation.py
index ae1259e..4c9ef64 100644
--- a/tetris_gymnasium/wrappers/observation.py
+++ b/tetris_gymnasium/wrappers/observation.py
@@ -8,10 +8,10 @@
 from tetris_gymnasium.envs import Tetris
 
 
-class CnnObservation(gym.ObservationWrapper):
-    """Observation wrapper that displays all observations (board, holder, queue) in a single 2D matrix, instead of a dictionary.
+class RgbObservation(gym.ObservationWrapper):
+    """Observation wrapper that displays all observations (board, holder, queue) as one single RGB Image.
 
-    The 2D matrix contains the board on the left, the queue on the top right and the holder on the bottom right.
+    The observation contains the board on the left, the queue on the top right and the holder on the bottom right.
     """
 
     def __init__(self, env: Tetris):
@@ -25,14 +25,15 @@ def __init__(self, env: Tetris):
                 env.unwrapped.width_padded
                 + max(env.unwrapped.holder.size, env.unwrapped.queue.size)
                 * env.unwrapped.padding,
+                3,
             ),
-            dtype=np.float32,
+            dtype=np.uint8,
         )
 
     def observation(self, observation):
-        """Wrapper that displays all observations (board, holder, queue) in a single 2D matrix.
+        """Observation wrapper that displays all observations (board, holder, queue) as one single RGB Image.
 
-        The 2D matrix contains the board on the left, the queue on the top right and the holder on the bottom right.
+        The observation contains the board on the left, the queue on the top right and the holder on the bottom right.
         """
         # Board
         board_obs = observation["board"]
@@ -56,7 +57,14 @@ def observation(self, observation):
         v_padding = np.ones((board_obs.shape[0] - 2 * max_size, max_len))
         cnn_extra = np.vstack((queue_obs, v_padding, holder_obs))
 
-        return np.hstack((board_obs, cnn_extra))
+        stack = np.hstack((board_obs, cnn_extra)).astype(np.integer)
+
+        # Convert to RGB
+        rgb = np.zeros((stack.shape[0], stack.shape[1], 3))
+        colors = np.array(list(p.color_rgb for p in self.pixels), dtype=np.uint8)
+        rgb[...] = colors[stack]
+
+        return rgb.astype(np.uint8)
 
     def render(self) -> "RenderFrame | list[RenderFrame] | None":
         """Renders the environment in various formats.
@@ -64,21 +72,11 @@ def render(self) -> "RenderFrame | list[RenderFrame] | None":
         This render function is different from the default as it uses the values from :func:`observation`  to render
         the environment.
         """
-        matrix = self.observation(self.env.unwrapped._get_obs()).astype(np.integer)
+        matrix = self.observation(self.env.unwrapped._get_obs())
 
-        if self.render_mode == "ansi":
-            char_field = np.where(matrix == 0, ".", matrix.astype(str))
-            field_str = "\n".join("".join(row) for row in char_field)
-            return field_str
         if self.render_mode == "human" or self.render_mode == "rgb_array":
-            # Initialize rgb array
-            rgb = np.zeros((matrix.shape[0], matrix.shape[1], 3), dtype=np.uint8)
-            # Render the board
-            colors = np.array(list(p.color_rgb for p in self.pixels), dtype=np.uint8)
-            rgb[...] = colors[matrix]
-
             if self.render_mode == "rgb_array":
-                return rgb
+                return matrix
 
             if self.render_mode == "human":
                 if self.env.unwrapped.window_name is None:
@@ -88,7 +86,8 @@ def render(self) -> "RenderFrame | list[RenderFrame] | None":
                     )
                     cv2.resizeWindow(self.env.unwrapped.window_name, 395, 250)
                 cv2.imshow(
-                    self.env.unwrapped.window_name, cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
+                    self.env.unwrapped.window_name,
+                    cv2.cvtColor(matrix, cv2.COLOR_RGB2BGR),
                 )
 
         return None