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 3871–3880 of 15113 papers

TitleStatusHype
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs—0
Policy Expansion for Bridging Offline-to-Online Reinforcement LearningCode1
Lower Bounds for Learning in Revealing POMDPs—0
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning—0
Selective Uncertainty Propagation in Offline RL—0
Sample Complexity of Kernel-Based Q-Learning—0
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement LearningCode2
Combining Deep Reinforcement Learning and Search with Generative Models for Game-Theoretic Opponent Modeling—0
Collaborating with language models for embodied reasoning—0
Internally Rewarded Reinforcement LearningCode1
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Benchmark Results

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