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 2061–2070 of 15113 papers

TitleStatusHype
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement LearningCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
Benchmarking Constraint Inference in Inverse Reinforcement LearningCode1
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Benchmark Results

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