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

Last update: Dec 23, 2022

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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

Owner

Scott Fujimoto

GitHub Repository

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