This repo contains a unified opensource code implementation for the Model-Based Reinforcement Learning methods.(@TJU). MBRL-Lib provides implementations of popular MBRL algorithms as examples of how to use this library. You can find them in the MBRL-Lib folder. We have collected some of the mainstream MBRL algorithms and made some code-level optimizations. Bringing these algorithms together in a unified framework can save the researchers time in finding comparative baselines without the need to search around for implementations. Currently, we have implemented Dreamer, MBPO,BMPO, MuZero, PlaNet, SampledMuZero, CaDM and we plan to keep increasing this list in the future. We will constantly update this repo to include new research made by TJU-DRL-Lab to ensure sufficient coverage and reliability. We hope in the future to present one of the most comprehensive Model-Based library so far , which covered most mainstream algorithms in Model-Based RL area. What' more, We want to cover as many interesting new directions as possible, and then divide it into the topic we listed above, to give you some inspiration and ideas for your RESEARCH. This repo will be constantly updated to include new researches made by TJU-RL-Lab. (The development of this repo is in progress at present.)
Reinforcement learning algorithms can be divided into two main categories: the first category is Model Free methods, which are able to learn a value function or a strategy function directly by interacting with the environment without modeling the environment; the second category is Model Based methods, which need to model the environment by interacting with it and then use the model to make action planning or strategy selection. The model free algorithm has achieved great results in many experimental scenarios and game environments due to its simple implementation and high asymptotic performance, but the sampling efficiency of the model free method is extremely low, resulting in a large number of interactions with the environment. However, the model free class of methods is extremely inefficient in terms of sampling efficiency and requires a lot of interaction with the environment. In contrast, the model-based algorithm models the environment and fits the dynamics model of the environment with high sampling efficiency.
The model of the environment is a representation model that explicitly contains knowledge about the environment or the task, and generally two types of models are included: a transition model or a dynamics model and the reward model. Once this model is modeled, it can be properly integrated into the interaction with the environment and the learning of strategies, as shown in the above figure.
The current classifications of the mainstream algorithms in the modern Model-Based RL area are orthogonal, which means some algorithms can be grouped into different categories according to different perspectives. In this branch, we focus on two key questions :How to Learn a Model and How to Utilize a Model.
How to Learn a Modelmainly focuses on how to build the environment model.How to Utilize a Modelcares about how to utilize the learned model.
From the perspective of action execution, we can also divide Model-Based RL into two categories: policy learning and planning.
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policy learningmainly focuses on training a parametric policy network explicitly. -
planningcares about how to optimize the action sequence implicitly.
There are many other classifications and we can list some of them here. From the perspective of the dynamics model, we can divide dynamics models into three categories:forward model、reverse/backward model, and inverse model. From the perspective of the estimation method, the methods can be categorized asparametric and non-parametric or exact and approximate. From the perspective of planning updating, the methods can be categorized as value update and policy update.
The current classifications of the mainstream algorithms in the modern Model-Based RL area are orthogonal, which means some algorithms can be grouped into different categories according to different perspectives. It’s quite hard to draw an accurate taxonomy of algorithms in the Model-Based RL area. So we think it would be more appropriate to give the algorithm to a specific topic rather than a simple classification. Ignoring the differences in specific methods, the purpose of MBRL algorithms can be more finely divided into four directions as follows: Reduce Model Error、Faster Planning、 Higher Tolerance to Model Error 、Scalability to Harder Problems. For the problem of How to Learn a Model, we can study reducing model error to learn a more accurate world model or learning a world model with higher tolerance to model error. For the problem of How to Utilize a Model, we can study faster planning with a learned model or the scalability of the learned model to harder problems.
Model-based reinforcement learning (MBRL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environments dynamics. The model of the environment is a representation model that explicitly contains knowledge about the environment or the task, and generally two types of models are included: a transition model or a dynamics model and the reward model. Once this model is modeled, it can be properly integrated into the interaction with the environment and the learning of strategies. MBRL is now becoming an increasingly promising direction in RL!
Research in model-based RL has not been very standardized. It is fairly common for authors to experiment with self-designed environments, and there are several separate lines of research, which are sometimes closed-sourced or not reproducible. And for this, we have collected some of the mainstream MBRL algorithms and made some code-level optimizations. Bringing these algorithms together in a unified framework can save the researchers time in finding comparative baselines without the need to search around for implementations.
With this repo and our research works, we want to draw the attention of RL community to studies on Model Based RL.
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For people who are insterested in model based RL, our introductions in this repo and our ZhiHu blog series can be a preliminary tutorial.
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For Researchers in model-based RL, we collect several separate lines of research, which are sometimes closed-sourced or not reproducible and make some code-level optimizations for the convinience to find comparative baselines without the need to search around for implementations.
We expect that our research thoughts and proposed topic for MBRL area can open up some new angles for future works on more advanced RL. What' more, We want to cover as many interesting new directions as possible, and then divide it into the topic we listed above, to give you some inspiration and ideas for your RESEARCH. Currently, we have implemented Dreamer, MBPO,BMPO, MuZero, PlaNet, SampledMuZero, CaDM and we plan to keep increasing this list in the future. We will constantly update this repo to include new research made by TJU-DRL-Lab to ensure sufficient coverage and reliability. We are also looking forward to feedback in any form to promote more in-depth researches. See more here.
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Vanilla Dreamer
Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination and learns behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model.
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ED2 Dreamer
Environment Dynamics Decomposition (ED2), a novel world model construction framework that models the environment in a decomposing manner.
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Vanilla MBPO
Model-Based Policy Optimization using short model-generated rollouts branched from real data has the benefits of more complicated model-based algorithms without the usual pitfalls.
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ED2 MBPO
Environment Dynamics Decomposition (ED2), a novel world model construction framework that models the environment in a decomposing manner.
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Mastering Atari , Go, Chess and Shogi by Planning with a Learned Model. MuZero is a series of work produced by DeepMind.
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Bidirectional Model-based Policy Optimization. a backward dynamics model to reduce the reliance on accuracy in forward model predictions called Bidirectional Model-based Policy Optimization (BMPO) to utilize both the forward model and backward model to generate short branched rollouts for policy optimization.
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Learning Latent Dynamics for Planning from Pixels. Deep Planning Network (PlaNet), a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space.
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Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning.A state-of-the-art model-based RL method for dynamics generalization.
The algorithms in this repo are all implemented python 3.5 (and versions above). Tensorflow 1.x and PyTorch are the main DL code frameworks we adopt in this repo with different choices in different algorithms.
First of all, we recommend the user to install anaconada and or venv for convenient management of different python envs.
In this repo, the following RL environments may be needed:
- OpenAI Gym (e.g., MuJoCo, Robotics)
- MinAtar
- xxxx
Note that each algorithm may use only one or several environments in the ones listed above. Please refer to the page of specific algorithm for concrete requirements.
To clone this repo:
git clone git@github.com:TJU-DRL-LAB/modelbased-rl.git
Note that this repo is a collection of multiple research branches (according to the taxonomy). Environments and code frameworks may differ among different branches. Thus, please follow the installation guidance provided in the specific branch you are insterested in.
We give some usage guidance about installing dependencies for beginners here, and also introduce how to modify the algorithm based on our implement version.
Here we introduce how to configure your own dataset and modify the algorithm based on your own design for .
First follow the vanilla Dreamer and MBPO method and install the coresponding dependencies (we introduced them in Dreamer/README.md and MBPO/README.md). Then run ED2-Dreamer or ED2-MBPO (following the detail operation in ED2-Dreamer/README.md or ED2-MBPO/README.md).
We integrated related works and codes into a unified framework, in order to make it easy to compare the main improvements between different works. For example, for Dreamer and ED2-Dreamer, we integrate the world model construction of both methods in a single moduel, and the same is true for other parts such as policies or wrappers. In this way, you can target the modifications you want to improve, and quickly verify and compare.
- Integrate codes of related work in unified framework
- Update a liscence
- Update the README files for each branches
- Check the vadality of codes to release
If you use our repo in your work, we ask that you cite our paper.
Here is an example BibTeX:
@article{wang2021ed2,
title={ED2: An Environment Dynamics Decomposition Framework for World Model Construction},
author={Wang, Cong and Yang, Tianpei and Hao, Jianye and Zheng, Yan and Tang, Hongyao and Barez, Fazl and Liu, Jinyi and Peng, Jiajie and Piao, Haiyin and Sun, Zhixiao},
journal={arXiv preprint arXiv:2112.02817},
year={2021}
}
@article{tjurllab22modelbasedrl,
author = {TJU RL Lab},
title = {A Unified Repo for Model-based Reinforcement Learning},
year = {2022},
url = {https://github.com/TJU-DRL-LAB/modelbased-rl},
}
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This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
[To add some acknowledgment]