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 3391–3400 of 15113 papers

TitleStatusHype
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning—0
Multi-Modal Transformer and Reinforcement Learning-based Beam Management—0
Curriculum Reinforcement Learning for Complex Reward Functions—0
Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies—0
Offline reinforcement learning for job-shop scheduling problems—0
Reinforcement Learning for Dynamic Memory AllocationCode0
Training Language Models to Critique With Multi-agent Feedback—0
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning—0
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
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Benchmark Results

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