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 1571–1580 of 15113 papers

TitleStatusHype
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement LearningCode1
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement LearningCode1
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Discrete Codebook World Models for Continuous ControlCode1
Discriminator-Weighted Offline Imitation Learning from Suboptimal DemonstrationsCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience ReplayCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
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Benchmark Results

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