# Offline RL

100 papers with code • 1 benchmarks • 6 datasets

## Libraries

Use these libraries to find Offline RL models and implementations## Datasets

## Most implemented papers

# 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.

# Decision Transformer: Reinforcement Learning via Sequence Modeling

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

# Offline Reinforcement Learning with Implicit Q-Learning

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.

# D4RL: Datasets for Deep Data-Driven Reinforcement Learning

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.

# A Minimalist Approach to Offline Reinforcement Learning

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

# 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.

# Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction.

# The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints

We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances.

# 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.

# NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

We evaluate existing offline RL algorithms on NeoRL and argue that the performance of a policy should also be compared with the deterministic version of the behavior policy, instead of the dataset reward.