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 7876–7900 of 15113 papers

TitleStatusHype
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real—0
Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems—0
Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning—0
Multi-agent Natural Actor-critic Reinforcement Learning Algorithms—0
Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm—0
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
Multi-agent Path Finding for Timed Tasks using Evolutionary Games—0
Multi-Agent Path Planning Using Deep Reinforcement Learning—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles—0
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
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Benchmark Results

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