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 10001–10025 of 15113 papers

TitleStatusHype
Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks—0
Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shaping—0
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph—0
Distributed Deep Q-Learning—0
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox—0
Distributed Deep Reinforcement Learning: An Overview—0
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids—0
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots—0
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning—0
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks—0
Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework—0
Distributed Ensembles of Reinforcement Learning Agents for Electricity Control—0
Distributed Learning on Heterogeneous Resource-Constrained Devices—0
Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control—0
Distributed Multi-Agent Deep Reinforcement Learning for Robust Coordination against Noise—0
Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC—0
Distributed Multitask Reinforcement Learning with Quadratic Convergence—0
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus—0
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach—0
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation—0
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control—0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching—0
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Benchmark Results

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