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 3621–3630 of 15113 papers

TitleStatusHype
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
Rethinking State Disentanglement in Causal Reinforcement Learning—0
Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning—0
SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning—0
Localized Observation Abstraction Using Piecewise Linear Spatial Decay for Reinforcement Learning in Combat Simulations—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Domain Adaptation for Offline Reinforcement Learning with Limited Samples—0
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement LearningCode0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
Using Part-based Representations for Explainable Deep Reinforcement Learning—0
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Benchmark Results

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