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 2811–2820 of 15113 papers

TitleStatusHype
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation—0
Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach—0
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households—0
Robust Offline Reinforcement learning with Heavy-Tailed RewardsCode0
Unsupervised Behavior Extraction via Random Intent Priors—0
Benchmark Generation Framework with Customizable Distortions for Image Classifier RobustnessCode0
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement LearningCode1
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Benchmark Results

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