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 1141–1150 of 15113 papers

TitleStatusHype
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for RoboticsCode1
Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 DiabetesCode1
Federated Reinforcement Learning with Environment HeterogeneityCode1
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading DecisionsCode1
Jump-Start Reinforcement LearningCode1
Inferring Rewards from Language in ContextCode1
Value Gradient weighted Model-Based Reinforcement LearningCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
Reinforcement Learning with Action-Free Pre-Training from VideosCode1
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Benchmark Results

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