PyTorch implementation of Munchausen Reinforcement Learning based on DQN and SAC. Handles discrete and continuous action spaces

Related tags

Deep Learning Exploring-Munchausen-Reinforcement-Learning
Overview

Exploring Munchausen Reinforcement Learning

This is the project repository of my team in the "Advanced Deep Learning for Robotics" course at TUM. Our project's topic is "Exploring Munchausen Reinforcement Learning" based on this paper.

For a detailed discussion, see the report and the final presentation.

Setup

  • Create a virtual environment.
  • Run pip3 install -r requirements.txt

Code Structure

This repository is structured as follows:

  • The directories M-DQN and M-SAC contain the implementations of the RL agents DQN and SAC extended with the Munchausen term, respectively.

  • The directories rl-baselines3-zoo contains a copy of this repository, where we included the implementations of M-DQN so that we can easily train and test the M-DQN agent on benchmark environments and also compare it to other classical agents. To do so, just follow the steps described in the original repository and insert M-DQN as the agent argument.

  • The directory particles-envcontains a modified version of this repository. The modified version contains code for a particles environment, where an agent wants to reach a goal, while avoiding obstacles. Besides, M-SAC agent is implemented and included in the code, so that it can be trained and compared to the classical SAC agent.

  • The directory action-gap contains implementation of callbacks for experiment manager of rl-baselines3-zoo which logs action-gap for tensorboard.

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Comments
  • how use pip3 in virtual env?

    how use pip3 in virtual env?

    I creat conda virtual,but cannt use pip3 to install requirements. and cannt install pip3 in conda vitrtual.so what you method to use pip3 in conda env?

    opened by wangchangquan 0
Owner
Mohamed Amine Ketata
Student in M.Sc "Robotics, Cognition, Intelligence" at the Technical University of Munich. Very passionate about Machine Learning, especially DL and RL.
Mohamed Amine Ketata
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