Offline RL
225 papers with code • 2 benchmarks • 6 datasets
Libraries
Use these libraries to find Offline RL models and implementationsDatasets
D4RL
RoboNet
RL Unplugged
NeoRL
Visuomotor affordance learning (VAL) robot interaction dataset
Latest papers
Compositional Conservatism: A Transductive Approach in Offline Reinforcement Learning
Our COCOA seeks both in-distribution anchors and differences by utilizing the learned reverse dynamics model, encouraging conservatism in the compositional input space for the policy or value function.
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
kvfrans/fre
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Can we pre-train a generalist agent from a large amount of unlabeled offline trajectories such that it can be immediately adapted to any new downstream tasks in a zero-shot manner?
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
This work offers a hands-on reference for the research progress in deep generative models for offline policy learning, and aims to inspire improved DGM-based offline RL or IL algorithms.
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces
zheng0428/more_
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Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions more easily, we propose a new perspective to the offline reinforcement learning (RL) challenge.
Stitching Sub-Trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL
rlatjddbs/ssd
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In this paper, we propose SSD (Sub-trajectory Stitching with Diffusion), a model-based offline GCRL method that leverages the conditional diffusion model to address these limitations.
SEABO: A Simple Search-Based Method for Offline Imitation Learning
dmksjfl/seabo
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Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the environment.
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
ruoqizzz/entropy-regularized-diffusion-policy-with-qensemble
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We show that such an SDE has a solution that we can use to calculate the log probability of the policy, yielding an entropy regularizer that improves the exploration of offline datasets.
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
To resolve this issue, we propose a simple yet effective modification that projects the backward gradient onto the normal plane of the forward gradient, resulting in an orthogonal-gradient update, a new learning rule for DICE-based methods.
Differentiable Tree Search in Latent State Space
In this work, we introduce Differentiable Tree Search (DTS), a novel neural network architecture that significantly strengthens the inductive bias by embedding the algorithmic structure of a best-first online search algorithm.
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Interestingly, we discover that via reachability analysis of safe-control theory, the hard safety constraint can be equivalently translated to identifying the largest feasible region given the offline dataset.
