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 591–600 of 15113 papers

TitleStatusHype
Debiased Contrastive LearningCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
Deep RL Agent for a Real-Time Action Strategy GameCode1
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
D2RL: Deep Dense Architectures in Reinforcement LearningCode1
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Benchmark Results

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