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 1351–1360 of 15113 papers

TitleStatusHype
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Controlling the Risk of Conversational Search via Reinforcement LearningCode1
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive SummarizationCode1
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated DrivingCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
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Benchmark Results

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