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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9976–10000 of 15113 papers

TitleStatusHype
Multi-Agent Reinforcement Learning in Cournot Games—0
Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution—0
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading—0
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation—0
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization—0
Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer—0
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management—0
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management—0
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—0
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning—0
Multi-Agent Reinforcement Learning with Multi-Step Generative Models—0
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids—0
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
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Benchmark Results

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