Add more Highway Enviroments

mo_gymnasium/envs/highway/__init__.py

@@ -4,3 +4,12 @@
 register(id="mo-highway-v0", entry_point="mo_gymnasium.envs.highway.highway:MOHighwayEnv", nondeterministic=True)
 
 register(id="mo-highway-fast-v0", entry_point="mo_gymnasium.envs.highway.highway:MOHighwayEnvFast", nondeterministic=True)
+
+register(id="mo-intersection-v0", entry_point="mo_gymnasium.envs.highway.intersection:MOIntersectionEnv", nondeterministic=True)
+
+register(id="mo-merge-v0", entry_point="mo_gymnasium.envs.highway.merge:MOMergeEnv", nondeterministic=True)
+
+register(id="mo-racetrack-v0", entry_point="mo_gymnasium.envs.highway.racetrack:MORacetrackEnv", nondeterministic=True)
+
+register(id="mo-roundabout-v0", entry_point="mo_gymnasium.envs.highway.roundabout:MORoundaboutEnv", nondeterministic=True)
+

mo_gymnasium/envs/highway/intersection.py

@@ -0,0 +1,44 @@
+import numpy as np
+from gymnasium.spaces import Box
+from gymnasium.utils import EzPickle
+from highway_env.envs import IntersectionEnv
+
+
+class MOIntersectionEnv(IntersectionEnv, EzPickle):
+    """
+    ## Description
+    Multi-objective version of the IntersectionEnv environment.
+
+    See [highway-env](https://github.com/eleurent/highway-env) for more information.
+
+    ## Reward Space
+    The reward is 4-dimensional:
+    - 0: high speed reward
+    - 1: arrived reward
+    - 2: collision reward
+    - 3: on road reward
+    """
+
+    def __init__(self, *args, **kwargs):
+        EzPickle.__init__(self, *args, **kwargs)
+
+        super().__init__(*args, **kwargs)
+        self.reward_space = Box(
+            low=np.array([0.0, 0.0, -1.0, 0.0]), high=np.array([1.0, 1.0, 0.0, 1.0]), shape=(4,), dtype=np.float64
+        )
+        self.reward_dim = 4
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+        rewards = info["rewards"]
+        vec_reward = np.array(
+            [
+                rewards["high_speed_reward"],
+                rewards["arrived_reward"],
+                -rewards["collision_reward"],
+                rewards["on_road_reward"],
+            ],
+            dtype=np.float64,
+        )
+        info["original_reward"] = reward
+        return obs, vec_reward, terminated, truncated, info

mo_gymnasium/envs/highway/merge.py

@@ -0,0 +1,46 @@
+import numpy as np
+from gymnasium.spaces import Box
+from gymnasium.utils import EzPickle
+from highway_env.envs import MergeEnv
+
+
+class MOMergeEnv(MergeEnv, EzPickle):
+    """
+    ## Description
+    Multi-objective version of the MergeEnv environment.
+
+    See [highway-env](https://github.com/eleurent/highway-env) for more information.
+
+    ## Reward Space
+    The reward is 5-dimensional:
+    - 0: high speed reward
+    - 1: right lane reward
+    - 2: collision reward
+    - 3: lane change reward
+    - 4: merging speed reward
+    """
+
+    def __init__(self, *args, **kwargs):
+        EzPickle.__init__(self, *args, **kwargs)
+
+        super().__init__(*args, **kwargs)
+        self.reward_space = Box(
+            low=np.array([-1.0, 0.0, -1.0, 0.0, 0.0]), high=np.array([1.0, 1.0, 0.0, 1.0, 1.0]), shape=(5,), dtype=np.float32
+        )
+        self.reward_dim = 5
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+        rewards = info["rewards"]
+        vec_reward = np.array(
+            [
+                np.clip(rewards["high_speed_reward"], -1.0, 1.0),
+                rewards["right_lane_reward"],
+                -rewards["collision_reward"],
+                rewards["lane_change_reward"],
+                np.clip(rewards["merging_speed_reward"], 0.0, 1.0),
+            ],
+            dtype=np.float32,
+        )
+        info["original_reward"] = reward
+        return obs, vec_reward, terminated, truncated, info

mo_gymnasium/envs/highway/racetrack.py

@@ -0,0 +1,44 @@
+import numpy as np
+from gymnasium.spaces import Box
+from gymnasium.utils import EzPickle
+from highway_env.envs import RacetrackEnv
+
+
+class MORacetrackEnv(RacetrackEnv, EzPickle):
+    """
+    ## Description
+    Multi-objective version of the RacetrackEnv environment.
+
+    See [highway-env](https://github.com/eleurent/highway-env) for more information.
+
+    ## Reward Space
+    The reward is 4-dimensional:
+    - 0: lane centering reward
+    - 1: action reward
+    - 2: collision reward
+    - 3: on road reward
+    """
+
+    def __init__(self, *args, **kwargs):
+        EzPickle.__init__(self, *args, **kwargs)
+
+        super().__init__(*args, **kwargs)
+        self.reward_space = Box(
+            low=np.array([0.0, 0.0, -1.0, 0.0]), high=np.array([1.0, 1.0, 0.0, 1.0]), shape=(4,), dtype=np.float32
+        )
+        self.reward_dim = 4
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+        rewards = info["rewards"]
+        vec_reward = np.array(
+            [
+                rewards["lane_centering_reward"],
+                rewards["action_reward"],
+                -rewards["collision_reward"],
+                rewards["on_road_reward"],
+            ],
+            dtype=np.float32,
+        )
+        info["original_reward"] = reward
+        return obs, vec_reward, terminated, truncated, info

mo_gymnasium/envs/highway/roundabout.py

@@ -0,0 +1,44 @@
+import numpy as np
+from gymnasium.spaces import Box
+from gymnasium.utils import EzPickle
+from highway_env.envs import RoundaboutEnv
+
+
+class MORoundaboutEnv(RoundaboutEnv, EzPickle):
+    """
+    ## Description
+    Multi-objective version of the RoundaboutEnv environment.
+
+    See [highway-env](https://github.com/eleurent/highway-env) for more information.
+
+    ## Reward Space
+    The reward is 4-dimensional:
+    - 0: high speed reward
+    - 1: on road reward
+    - 2: collision reward
+    - 3: lane change reward
+    """
+
+    def __init__(self, *args, **kwargs):
+        EzPickle.__init__(self, *args, **kwargs)
+
+        super().__init__(*args, **kwargs)
+        self.reward_space = Box(
+            low=np.array([0.0, 0.0, -1.0, 0.0]), high=np.array([1.0, 1.0, 0.0, 1.0]), shape=(4,), dtype=np.float32
+        )
+        self.reward_dim = 4
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = super().step(action)
+        rewards = info["rewards"]
+        vec_reward = np.array(
+            [
+                rewards["high_speed_reward"],
+                rewards["on_road_reward"],
+                -rewards["collision_reward"],
+                rewards["lane_change_reward"],
+            ],
+            dtype=np.float32,
+        )
+        info["original_reward"] = reward
+        return obs, vec_reward, terminated, truncated, info