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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2251–2275 of 15113 papers

TitleStatusHype
Critic-Guided Decoding for Controlled Text GenerationCode1
Reinformer: Max-Return Sequence Modeling for Offline RLCode1
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
Bridging Imagination and Reality for Model-Based Deep Reinforcement LearningCode1
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
An Inductive Bias for Distances: Neural Nets that Respect the Triangle InequalityCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
Bridging RL Theory and Practice with the Effective HorizonCode1
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
Bridging State and History Representations: Understanding Self-Predictive RLCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement LearningCode0
Learning Goal-Oriented Visual Dialog via Tempered Policy GradientCode0
Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral SimilaritiesCode0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
A Semi-Supervised Approach for Low-Resourced Text GenerationCode0
Behavior Prior Representation learning for Offline Reinforcement LearningCode0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
Learning from Trajectories via Subgoal DiscoveryCode0
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement LearningCode0
Behavior Estimation from Multi-Source Data for Offline Reinforcement LearningCode0
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Benchmark Results

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