DQN Replay Dataset

6 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Conservative Q-Learning for Offline Reinforcement Learning

aviralkumar2907/CQL NeurIPS 2020

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.

Acme: A Research Framework for Distributed Reinforcement Learning

deepmind/acme 1 Jun 2020

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.

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

deepmind/deepmind-research 24 Jun 2020

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.

Revisiting Fundamentals of Experience Replay

google-research/google-research ICML 2020

Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding.

An Optimistic Perspective on Offline Reinforcement Learning

google-research/batch_rl 10 Jul 2019

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