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

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Showing 7351–7375 of 15113 papers

TitleStatusHype
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
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models—0
Few-Shot Goal Inference for Visuomotor Learning and Planning—0
Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning—0
Few-shot model-based adaptation in noisy conditions—0
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases—0
Few-Shot Preference Learning for Human-in-the-Loop RL—0
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning—0
Few-Shot Teamwork—0
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Benchmark Results

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