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 3711–3720 of 15113 papers

TitleStatusHype
Neural Laplace Control for Continuous-time Delayed SystemsCode1
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual ExplanationsCode1
EvoTorch: Scalable Evolutionary Computation in PythonCode3
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs—0
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains—0
Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement LearningCode1
To the Noise and Back: Diffusion for Shared Autonomy—0
Concept Learning for Interpretable Multi-Agent Reinforcement Learning—0
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Benchmark Results

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