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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.

DQN REPLAY DATASET Q-LEARNING

Acme: A Research Framework for Distributed Reinforcement Learning

1 Jun 2020deepmind/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.

DQN REPLAY DATASET

An Optimistic Perspective on Offline Reinforcement Learning

10 Jul 2019google-research/batch_rl

The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.

ATARI GAMES DQN REPLAY DATASET Q-LEARNING

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.

CONTINUOUS CONTROL DQN REPLAY DATASET Q-LEARNING