SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 551–575 of 15113 papers

TitleStatusHype
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement LearningCode1
FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement LearningCode1
SUBER: An RL Environment with Simulated Human Behavior for Recommender SystemsCode1
Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement LearningCode1
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-ThoughtCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Reinforcement Learning in Dynamic Treatment Regimes Needs Critical ReexaminationCode1
DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment RegimeCode1
Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RLCode1
Q-value Regularized Transformer for Offline Reinforcement LearningCode1
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing ProblemsCode1
Rethinking Transformers in Solving POMDPsCode1
Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step OptimizationCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning RateCode1
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
Multi-turn Reinforcement Learning from Preference Human FeedbackCode1
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement LearningCode1
Maximum Entropy Reinforcement Learning via Energy-Based Normalizing FlowCode1
Knowledge Graph Reasoning with Self-supervised Reinforcement LearningCode1
CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality ResearchCode1
Feasibility Consistent Representation Learning for Safe Reinforcement LearningCode1
Reinformer: Max-Return Sequence Modeling for Offline RLCode1
Value Augmented Sampling for Language Model Alignment and PersonalizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified