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 20512060 of 15113 papers

TitleStatusHype
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement LearningCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
An Application of Deep Reinforcement Learning to Algorithmic TradingCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
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Benchmark Results

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