Offline RL
234 papers with code • 2 benchmarks • 7 datasets
Libraries
Use these libraries to find Offline RL models and implementationsDatasets
D4RL
RoboNet
NeoRL
RL Unplugged
Visuomotor affordance learning (VAL) robot interaction dataset
NeoRL-2
Latest papers
DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning, which is generally solved by advantage weighted regression (AWR).
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL
We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL.
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
Improbable-AI/dw-offline-rl
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NeurIPS 2023
We argue this is due to an assumption made by current offline RL algorithms of staying close to the trajectories in the dataset.
Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
quantumiracle/consistency_model_for_reinforcement_learning
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We propose to apply the consistency model as an efficient yet expressive policy representation, namely consistency policy, with an actor-critic style algorithm for three typical RL settings: offline, offline-to-online and online.
Zero-Shot Reinforcement Learning from Low Quality Data
enjeeneer/conservative-world-models
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Zero-shot reinforcement learning (RL) promises to provide agents that can perform any task in an environment after an offline, reward-free pre-training phase.
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
thu-rllab/CFCQL
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NeurIPS 2023
Offline multi-agent reinforcement learning is challenging due to the coupling effect of both distribution shift issue common in offline setting and the high dimension issue common in multi-agent setting, making the action out-of-distribution (OOD) and value overestimation phenomenon excessively severe.
VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments using offline Reinforcement Learning (RL).
Reasoning with Latent Diffusion in Offline Reinforcement Learning
ldcq/ldcq
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However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation errors arising due to a lack of support in the dataset.
ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
In safety-critical domains such as autonomous vehicles, offline deep reinforcement learning (offline DRL) is frequently used to train models on pre-collected datasets, as opposed to training these models by interacting with the real-world environment as the online DRL.
Model-based Offline Policy Optimization with Adversarial Network
Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization.
