Offline RL Edit Task
Playing Games • General Reinforcement Learning
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Greatest papers with code
Behavior Regularized Offline Reinforcement Learning
• google-research/google-research
• 
In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment.
RL Unplugged: 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.
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.
DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs
ICLR 2021
• maximecb/gym-miniworld
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We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience.
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also known as batch RL, refers to the setting where a policy must be learned from a static dataset, without additional online data collection.
An Optimistic Perspective on Offline Deep Reinforcement Learning
ICML 2020
• google-research/batch_rl
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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.
Human-centric Dialog Training via Offline Reinforcement Learning
EMNLP 2020
• natashamjaques/neural_chat
•
We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL).
Conservative Q-Learning for Offline Reinforcement Learning
NeurIPS 2020
• aviralkumar2907/CQL
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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.
MOPO: Model-based Offline Policy Optimization
We also characterize the trade-off between the gain and risk of leaving the support of the batch data.