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 711–720 of 15113 papers

TitleStatusHype
SafeDreamer: Safe Reinforcement Learning with World ModelsCode1
Robotic Manipulation Datasets for Offline Compositional Reinforcement LearningCode1
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control TasksCode1
Payload-Independent Direct Cost Learning for Image SteganographyCode1
RLTF: Reinforcement Learning from Unit Test FeedbackCode1
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive RecommendationCode1
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Model-Bellman Inconsistency for Model-based Offline Reinforcement LearningCode1
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Benchmark Results

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