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 1731–1740 of 15113 papers

TitleStatusHype
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks—0
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network—0
ProSpec RL: Plan Ahead, then Execute—0
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations—0
Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems—0
Evolution of cooperation in the public goods game with Q-learning—0
A Method for Fast Autonomy Transfer in Reinforcement Learning—0
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
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Benchmark Results

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