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 1121–1130 of 15113 papers

TitleStatusHype
Preventive Energy Management for Distribution Systems Under Uncertain Events: A Deep Reinforcement Learning Approach—0
Provably Efficient Exploration in Reward Machines with Low Regret—0
Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning—0
A Reinforcement Learning-Based Task Mapping Method to Improve the Reliability of Clustered Manycores—0
xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of ExplainabilityCode0
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMsCode5
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RLCode0
Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search—0
Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization—0
Reinforcement Learning for Motor Control: A Comprehensive Review—0
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Benchmark Results

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