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 491–500 of 15113 papers

TitleStatusHype
Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via Reward Neutralization—0
Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions—0
Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
Actor-Critics Can Achieve Optimal Sample Efficiency—0
The Steganographic Potentials of Language Models—0
Online Phase Estimation of Human Oscillatory Motions using Deep Learning—0
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement LearningCode3
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Benchmark Results

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