DQN Replay Dataset
6 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find DQN Replay Dataset models and implementationsLatest papers
Revisiting Fundamentals of Experience Replay
google-research/google-research
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ICML 2020
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
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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.
Conservative Q-Learning for Offline Reinforcement Learning
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
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
An Optimistic Perspective on Offline Reinforcement Learning
The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.
