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'
This repository contains the implementation for the AoS paper "Testing Stationarity and Change Point Detection in Reinforcement Learning" in Python (and R for plotting).
limengbinggz/cusum-rl की मुख्य विशेषताएं हैं: Offline RL Implementations।
limengbinggz/cusum-rl के ओपन-सोर्स विकल्पों में शामिल हैं: 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…
