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 7901–7925 of 15113 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study—0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks—0
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads—0
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning—0
Multi-agent reinforcement learning for intent-based service assurance in cellular networks—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching—0
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense—0
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
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
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Benchmark Results

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