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 3301–3325 of 15113 papers

TitleStatusHype
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
A Succinct Summary of Reinforcement Learning—0
Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems—0
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control—0
Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach—0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Deep reinforcement learning for quantum multiparameter estimation—0
Decentralized model-free reinforcement learning in stochastic games with average-reward objective—0
A Gentle Lecture Note on Filtrations in Reinforcement Learning—0
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems—0
AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Decentralized Multi-Robot Formation Control Using Reinforcement Learning—0
Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones—0
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions—0
Deep reinforcement learning for RAN optimization and control—0
Decentralized Safe Reinforcement Learning for Voltage Control—0
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
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Benchmark Results

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