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

Showing 391400 of 15113 papers

TitleStatusHype
A simple but strong baseline for online continual learning: Repeated Augmented RehearsalCode1
A Comprehensive Survey of Data Augmentation in Visual Reinforcement LearningCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
A Composable Specification Language for Reinforcement Learning TasksCode1
A Boolean Task Algebra for Reinforcement LearningCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Controlling the Risk of Conversational Search via Reinforcement LearningCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
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Benchmark Results

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