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 3181–3190 of 15113 papers

TitleStatusHype
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management—0
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Model-Bellman Inconsistency for Model-based Offline Reinforcement LearningCode1
Decentralized Motor Skill Learning for Complex Robotic Systems—0
Navigation of micro-robot swarms for targeted delivery using reinforcement learning—0
Comparing Reinforcement Learning and Human Learning using the Game of Hidden Rules—0
ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch—0
Probabilistic Constraint for Safety-Critical Reinforcement Learning—0
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning—0
Safety-Aware Task Composition for Discrete and Continuous Reinforcement Learning—0
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Benchmark Results

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