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 3791–3800 of 15113 papers

TitleStatusHype
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Average-Reward Reinforcement Learning with Trust Region Methods—0
A model-based approach to meta-Reinforcement Learning: Transformers and tree search—0
Deep Reinforcement Learning of Transition States—0
Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries—0
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos—0
Deep reinforcement learning on a multi-asset environment for trading—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
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Benchmark Results

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