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 7926–7950 of 15113 papers

TitleStatusHype
Emergent Social Learning via Multi-agent Reinforcement Learning—0
Multiagent Soft Q-Learning—0
Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective—0
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems—0
Multiagent Value Iteration Algorithms in Dynamic Programming and Reinforcement Learning—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning—0
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning—0
Multibit Tries Packet Classification with Deep Reinforcement Learning—0
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition—0
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
Multi-condition multi-objective optimization using deep reinforcement learning—0
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach—0
Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning—0
Multi-Fidelity Policy Gradient Algorithms—0
Multi-fidelity reinforcement learning framework for shape optimization—0
Multifidelity Reinforcement Learning with Control Variates—0
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach—0
Multi-focus Attention Network for Efficient Deep Reinforcement Learning—0
Multi-Issue Bargaining With Deep Reinforcement Learning—0
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning—0
Multi-level Explanation of Deep Reinforcement Learning-based Scheduling—0
Multi-Level Policy and Reward Reinforcement Learning for Image Captioning—0
Multi-market Energy Optimization with Renewables via Reinforcement Learning—0
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach—0
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Benchmark Results

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