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 3151–3160 of 15113 papers

TitleStatusHype
Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading—0
Provably Efficient Exploration in Reward Machines with Low Regret—0
xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of ExplainabilityCode0
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
Multimodal Deep Reinforcement Learning for Portfolio Optimization—0
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities—0
Reinforcement Learning for Motor Control: A Comprehensive Review—0
Environment Descriptions for Usability and Generalisation in Reinforcement Learning—0
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Benchmark Results

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