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 1821–1830 of 15113 papers

TitleStatusHype
Reasoning with Latent Diffusion in Offline Reinforcement LearningCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
Reduced Policy Optimization for Continuous Control with Hard ConstraintsCode1
Regularized Softmax Deep Multi-Agent Q-LearningCode1
Regulatory DNA sequence Design with Reinforcement LearningCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
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Benchmark Results

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