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 3371–3380 of 15113 papers

TitleStatusHype
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning—0
Constrained Proximal Policy Optimization—0
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Policy Representation via Diffusion Probability Model for Reinforcement LearningCode1
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search—0
Lagrangian-based online safe reinforcement learning for state-constrained systems—0
Offline Primal-Dual Reinforcement Learning for Linear MDPs—0
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and PracticeCode0
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Benchmark Results

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