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 4011–4020 of 15113 papers

TitleStatusHype
Queue-based Eco-Driving at Roundabouts with Reinforcement Learning—0
Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning—0
Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot CollaborationCode0
Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement LearningCode0
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning—0
Towards Generalist Robot Learning from Internet Video: A Survey—0
Reinforcement Learning Problem Solving with Large Language Models—0
Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies—0
Towards Generalizable Agents in Text-Based Educational Environments: A Study of Integrating RL with LLMs—0
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty—0
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Benchmark Results

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