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 401–410 of 15113 papers

TitleStatusHype
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial MarketsCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
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Benchmark Results

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