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 4581–4590 of 15113 papers

TitleStatusHype
Hyperparameter Optimization for Multi-Objective Reinforcement Learning—0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning—0
Finetuning Offline World Models in the Real World—0
Fractal Landscapes in Policy Optimization—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation—0
Enhancing Robotic Manipulation: Harnessing the Power of Multi-Task Reinforcement Learning and Single Life Reinforcement Learning in Meta-World—0
Corruption-Robust Offline Reinforcement Learning with General Function ApproximationCode0
Diverse Priors for Deep Reinforcement Learning—0
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare—0
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Benchmark Results

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