Offline RL

270 papers with code • 2 benchmarks • 7 datasets

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Libraries

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Most implemented papers

Decision Transformer: Reinforcement Learning via Sequence Modeling

kzl/decision-transformer NeurIPS 2021

In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling.

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.

Offline Reinforcement Learning with Implicit Q-Learning

rail-berkeley/rlkit 12 Oct 2021

The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state.

Reformer: The Efficient Transformer

google/trax ICLR 2020

Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences.

A Minimalist Approach to Offline Reinforcement Learning

sfujim/TD3_BC NeurIPS 2021

Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data.

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

rail-berkeley/offline_rl 15 Apr 2020

In this work, we introduce benchmarks specifically designed for the offline setting, guided by key properties of datasets relevant to real-world applications of offline RL.

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

idiap/fast-transformers ICML 2020

Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input's length, they are prohibitively slow for very long sequences.

Rethinking Attention with Performers

google-research/google-research ICLR 2021

We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness.

MOPO: Model-based Offline Policy Optimization

tianheyu927/mopo NeurIPS 2020

We also characterize the trade-off between the gain and risk of leaving the support of the batch data.

Acme: A Research Framework for Distributed Reinforcement Learning

google-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.