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 2311–2320 of 15113 papers

TitleStatusHype
Conditional Kernel Imitation Learning for Continuous State Environments—0
Conditions on Features for Temporal Difference-Like Methods to Converge—0
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules—0
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning—0
Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies—0
Analysis and Improvement of Policy Gradient Estimation—0
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts—0
Adaptive Temporal Difference Learning with Linear Function Approximation—0
Concept-modulated model-based offline reinforcement learning for rapid generalization—0
Bayesian Reinforcement Learning in Factored POMDPs—0
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Benchmark Results

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