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 1151–1160 of 15113 papers

TitleStatusHype
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
SUBER: An RL Environment with Simulated Human Behavior for Recommender SystemsCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
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Benchmark Results

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