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 2831–2840 of 15113 papers

TitleStatusHype
Controlled Decoding from Language Models—0
Privately Aligning Language Models with Reinforcement Learning—0
TD-MPC2: Scalable, Robust World Models for Continuous ControlCode2
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
Hyperparameter Optimization for Multi-Objective Reinforcement Learning—0
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning—0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning—0
Fractal Landscapes in Policy Optimization—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Finetuning Offline World Models in the Real World—0
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Benchmark Results

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