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 9951–9975 of 15113 papers

TitleStatusHype
Multi-agent query reformulation: Challenges and the role of diversity—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—0
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer—0
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms—0
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning—0
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward—0
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration—0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
Multi-agent Reinforcement Learning for Networked System Control—0
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
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Benchmark Results

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