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Found 7 papers, 1 papers with code
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
This work offers a hands-on reference for the research progress in deep generative models for offline policy learning, and aims to inspire improved DGM-based offline RL or IL algorithms.
Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
Through thorough theoretical analysis, we demonstrate that the quantum advantage in mean estimation leads to exponential advancements in regret guarantees for infinite horizon Reinforcement Learning.
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning
This improvement is a key to the significant regret improvement in quantum reinforcement learning.
Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift
Federated Learning (FL) is an emerging domain in the broader context of artificial intelligence research.
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point
CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility.
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming
We establish that the sample complexity to obtain near-globally optimal solutions matches tight dependencies on the cardinality of the state and action spaces, and exhibits classical scalings with respect to the network in accordance with multi-agent optimization.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Communication Efficient Parallel Reinforcement Learning
We provide \NAM\ which runs at each agent and prove that the total cumulative regret of $M$ agents is upper bounded as $\Tilde{O}(DS\sqrt{MAT})$ for a Markov Decision Process with diameter $D$, number of states $S$, and number of actions $A$.
