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Benchmarks
Greatest papers with code
Revisiting Fundamentals of Experience Replay
ICML 2020
• google-research/google-research
• 
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding.
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
• deepmind/deepmind-research
•
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
Acme: A Research Framework for Distributed Reinforcement Learning
• deepmind/acme
• 
Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.
An Optimistic Perspective on Offline Deep Reinforcement Learning
ICML 2020
• google-research/batch_rl
•
The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.
An Optimistic Perspective on Offline Reinforcement Learning
The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.
Conservative Q-Learning for Offline Reinforcement Learning
NeurIPS 2020
• aviralkumar2907/CQL
• 
We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees.