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 1571–1580 of 15113 papers

TitleStatusHype
SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience—0
MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safety—0
Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning—0
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate ControlCode1
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning—0
Training Language Models to Self-Correct via Reinforcement LearningCode2
The Central Role of the Loss Function in Reinforcement Learning—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling—0
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Benchmark Results

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