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 1231–1240 of 15113 papers

TitleStatusHype
RL-MILP Solver: A Reinforcement Learning Approach for Solving Mixed-Integer Linear Programs with Graph Neural Networks—0
Solving Rubik's Cube Without Tricky Sampling—0
HVAC-DPT: A Decision Pretrained Transformer for HVAC Control—0
Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in FeedCode0
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning—0
Convex Regularization and Convergence of Policy Gradient Flows under Safety Constraints—0
A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges—0
Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning—0
ScaleViz: Scaling Visualization Recommendation Models on Large Data—0
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics—0
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Benchmark Results

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