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 1491–1500 of 15113 papers

TitleStatusHype
Towards mental time travel: a hierarchical memory for reinforcement learning agentsCode1
Goal Misgeneralization in Deep Reinforcement LearningCode1
Robust Value Iteration for Continuous Control TasksCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous DrivingCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement LearningCode1
Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection StrategiesCode1
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point DetectionCode1
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Benchmark Results

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