Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL

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Deep Learning TD3_BC
Overview

A Minimalist Approach to Offline Reinforcement Learning

TD3+BC is a simple approach to offline RL where only two changes are made to TD3: (1) a weighted behavior cloning loss is added to the policy update and (2) the states are normalized. Unlike competing methods there are no changes to architecture or underlying hyperparameters.

Usage

Paper results were collected with MuJoCo 1.50 (and mujoco-py 1.50.1.1) in OpenAI gym 0.17.0 with the D4RL datasets. Networks are trained using PyTorch 1.4.0 and Python 3.6.

The paper results can be reproduced by running:

./run_experiments.sh
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Comments
  • Questions about performance metric d4rl_score.

    Questions about performance metric d4rl_score.

    I run the code in halfcheetah-expert-v0, and it seems to work well, but its performance metric d4rl_score is only about 1.1-1.2, and the result of the paper is about 110-120, I am confused. (my mujoco version is 200)

    opened by TianQi-777 2
  • The results in Antmaze

    The results in Antmaze

    Hi,

    I would like to ask the setting about the experiments in Antmaze. Should I need to tune the hyparameters for mujoco locomotion?

    I find I cannot reproduce the results about Antmaze in the paper.

    Best

    opened by lucasliunju 0
Owner
Scott Fujimoto
Scott Fujimoto
GitHub
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