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 1591–1600 of 15113 papers

TitleStatusHype
Distributional Reinforcement Learning with Unconstrained Monotonic Neural NetworksCode1
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous ControlCode1
Learning Robust State Abstractions for Hidden-Parameter Block MDPsCode1
Multi-Task Reinforcement Learning with Context-based RepresentationsCode1
Distributional Reinforcement Learning via Moment MatchingCode1
Multi Type Mean Field Reinforcement LearningCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience ReplayCode1
Generalized Decision Transformer for Offline Hindsight Information MatchingCode1
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Benchmark Results

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