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 2601–2610 of 15113 papers

TitleStatusHype
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
Adaptive Diffusion Policy Optimization for Robotic ManipulationCode0
Preference Optimization for Combinatorial Optimization Problems—0
DSADF: Thinking Fast and Slow for Decision Making—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
Reinforcement Learning-based Fault-Tolerant Control for Quadrotor with Online Transformer Adaptation—0
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic RewardCode0
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning—0
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning—0
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning—0
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Benchmark Results

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