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 10826–10850 of 15113 papers

TitleStatusHype
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning—0
Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps—0
Federated Offline Reinforcement Learning—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Federated Reinforcement Distillation with Proxy Experience Memory—0
Federated Deep Reinforcement Learning—0
Federated Reinforcement Learning at the Edge—0
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms—0
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control—0
Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning—0
Federated Reinforcement Learning: Techniques, Applications, and Open Challenges—0
Federated Transfer Reinforcement Learning for Autonomous Driving—0
FedHQL: Federated Heterogeneous Q-Learning—0
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
Feedback-Based Tree Search for Reinforcement Learning—0
Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning—0
FetchBot: Object Fetching in Cluttered Shelves via Zero-Shot Sim2Real—0
Feudal Dialogue Management with Jointly Learned Feature Extractors—0
Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Feudal Reinforcement Learning by Reading Manuals—0
Feudal Reinforcement Learning for Dialogue Management in Large Domains—0
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Benchmark Results

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