DQN Replay Dataset

6 papers with code • 0 benchmarks • 0 datasets

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Use these libraries to find DQN Replay Dataset models and implementations

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

These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.

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.