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 3591–3600 of 15113 papers

TitleStatusHype
Actor-Critic learning for mean-field control in continuous time—0
Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication—0
Path Planning using Reinforcement Learning: A Policy Iteration Approach—0
Kernel Density Bayesian Inverse Reinforcement LearningCode0
Deploying Offline Reinforcement Learning with Human Feedback—0
Visual-Policy Learning through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks—0
Transformer-based World Models Are Happy With 100k InteractionsCode1
Synthetic Experience ReplayCode1
Behavioral Differences is the Key of Ad-hoc Team Cooperation in Multiplayer Games Hanabi—0
The tree reconstruction game: phylogenetic reconstruction using reinforcement learning—0
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Benchmark Results

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