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

Showing 16311640 of 15113 papers

TitleStatusHype
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Modeling Protagonist Emotions for Emotion-Aware StorytellingCode1
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement LearningCode1
Model Primitive Hierarchical Lifelong Reinforcement LearningCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
MoDem: Accelerating Visual Model-Based Reinforcement Learning with DemonstrationsCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement LearningCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
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Benchmark Results

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