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 421–430 of 15113 papers

TitleStatusHype
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
A Benchmark Environment Motivated by Industrial Control ProblemsCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
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Benchmark Results

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