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 6526–6550 of 15113 papers

TitleStatusHype
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
Distributed Reinforcement Learning for Robot Teams: A Review—0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
Distributed Reinforcement Learning via Gossip—0
Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management—0
Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning—0
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning—0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning—0
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control—0
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Benchmark Results

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