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 1721–1730 of 15113 papers

TitleStatusHype
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based NavigationCode1
Population-Guided Parallel Policy Search for Reinforcement LearningCode1
Enhancing RL Safety with Counterfactual LLM ReasoningCode1
HackAtari: Atari Learning Environments for Robust and Continual Reinforcement LearningCode1
Entity-Centric Reinforcement Learning for Object Manipulation from PixelsCode1
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous DrivingCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
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Benchmark Results

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