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 3061–3070 of 15113 papers

TitleStatusHype
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
A survey on intrinsic motivation in reinforcement learning—0
A Survey on Interpretable Reinforcement Learning—0
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations—0
A Survey on GUI Agents with Foundation Models Enhanced by Reinforcement Learning—0
Adaptation of Quadruped Robot Locomotion with Meta-Learning—0
A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback—0
A Survey on Dialog Management: Recent Advances and Challenges—0
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management—0
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Benchmark Results

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