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 691–700 of 15113 papers

TitleStatusHype
Active Reinforcement Learning for Robust Building ControlCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-RescueCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learningCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
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Benchmark Results

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