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 451–460 of 15113 papers

TitleStatusHype
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement LearningCode3
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning—0
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic RewardCode0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning—0
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem SolvingCode2
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains—0
Measuring General Intelligence with Generated GamesCode1
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Benchmark Results

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