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 3331–3340 of 15113 papers

TitleStatusHype
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
Deciding What's Fair: Challenges of Applying Reinforcement Learning in Online Marketplaces—0
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning—0
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon—0
Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search—0
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
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Benchmark Results

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