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 2581–2590 of 15113 papers

TitleStatusHype
Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents—0
Autonomous Satellite Docking via Adaptive Optimal Output Rregulation: A Reinforcement Learning Approach—0
A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning—0
Cooperation and Reputation Dynamics with Reinforcement Learning—0
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards—0
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning—0
Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance—0
Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach—0
Autonomous Resource Management in Construction Companies Using Deep Reinforcement Learning Based on IoT—0
Adaptive optimal training of animal behavior—0
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Benchmark Results

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