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

Showing 731740 of 15113 papers

TitleStatusHype
Actor-Critic Reinforcement Learning for Control with Stability GuaranteeCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
Batch Exploration with Examples for Scalable Robotic Reinforcement LearningCode1
BEAR: Physics-Principled Building Environment for Control and Reinforcement LearningCode1
Bayesian Generational Population-Based TrainingCode1
Distilling Reinforcement Learning Algorithms for In-Context Model-Based PlanningCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing ProblemCode1
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Benchmark Results

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