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 4401–4410 of 15113 papers

TitleStatusHype
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning—0
A Survey of Reinforcement Learning from Human Feedback—0
Optimizing Heat Alert Issuance with Reinforcement LearningCode0
Maximum entropy GFlowNets with soft Q-learning—0
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles—0
Parameterized Projected Bellman OperatorCode0
Optimal coordination of resources: A solution from reinforcement learning—0
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game—0
Neural Network Approximation for Pessimistic Offline Reinforcement Learning—0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation—0
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Benchmark Results

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