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 3831–3840 of 15113 papers

TitleStatusHype
Multi-Task Recommendations with Reinforcement LearningCode1
Transfer learning for process design with reinforcement learning—0
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Adaptive Aggregation for Safety-Critical Control—0
Online Reinforcement Learning with Uncertain Episode Lengths—0
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective—0
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR—0
Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement LearningCode0
DITTO: Offline Imitation Learning with World Models—0
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Benchmark Results

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