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 3111–3120 of 15113 papers

TitleStatusHype
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks—0
Mode-constrained Model-based Reinforcement Learning via Gaussian ProcessesCode0
Reinforcement Learning -based Adaptation and Scheduling Methods for Multi-source DASHCode0
Offline Reinforcement Learning with On-Policy Q-Function Regularization—0
Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning—0
Submodular Reinforcement LearningCode1
Counterfactual Explanation Policies in RL—0
Settling the Sample Complexity of Online Reinforcement Learning—0
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation—0
Unbiased Weight Maximization—0
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Benchmark Results

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