Update (07/17): We have released a cleaner implementation of BEAR on top of rlkit at: https://github.com/rail-berkeley/d4rl_evaluations, which goes with the latest version of the D4RL paper. We would encourage all users to use this new implementation as compared to this repo. We made…
Code for Conservative Q-Learning for Offline Reinforcement Learning (https://arxiv.org/abs/2006.04779)
Author implementation of 'Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations'
Dependencies can be installed with the following command:
Die Hauptfunktionen von tung-nd/cwbc sind: Offline RL Implementations.
Open-Source-Alternativen zu tung-nd/cwbc sind unter anderem: aviralkumar2907/bear — Update (07/17): We have released a cleaner implementation of BEAR on top of rlkit at:… aviralkumar2907/cql — Code for Conservative Q-Learning for Offline Reinforcement Learning (https://arxiv.org/abs/2006.04779). avisingh599/cog — This repository accompanies the following paper:. danieltakeshi/dcur — This is the code used for the paper:. eladsar/rbi — Implementation of distributed RL algorithms:. albertwilcox/mcac — Author implementation of 'Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from…
