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 3151–3175 of 15113 papers

TitleStatusHype
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation—0
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets—0
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty—0
Combining model-predictive control and predictive reinforcement learning for stable quadrupedal robot locomotion—0
SafeDreamer: Safe Reinforcement Learning with World ModelsCode1
Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative—0
Robotic Manipulation Datasets for Offline Compositional Reinforcement LearningCode1
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control TasksCode1
Transformers in Reinforcement Learning: A Survey—0
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior—0
Payload-Independent Direct Cost Learning for Image SteganographyCode1
Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement LearningCode0
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing—0
Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)Code0
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive RecommendationCode1
RLTF: Reinforcement Learning from Unit Test FeedbackCode1
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning—0
Investigating the Edge of Stability Phenomenon in Reinforcement Learning—0
A User Study on Explainable Online Reinforcement Learning for Adaptive Systems—0
Active Collection of Well-Being and Health Data in Mobile DevicesCode0
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningCode1
When Do Transformers Shine in RL? Decoupling Memory from Credit AssignmentCode2
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback—0
Offline Reinforcement Learning with Imbalanced Datasets—0
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance—0
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Benchmark Results

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