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 3181–3190 of 15113 papers

TitleStatusHype
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions—0
Multi-Task Reinforcement Learning for Quadrotors—0
CLIP-RLDrive: Human-Aligned Autonomous Driving via CLIP-Based Reward Shaping in Reinforcement Learning—0
Equivariant Action Sampling for Reinforcement Learning and Planning—0
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents—0
Using machine learning to inform harvest control rule design in complex fishery settingsCode0
MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning—0
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation—0
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Benchmark Results

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