Learning Automata as Building Blocks for MARL

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=3-_LSemCbSw


Duration: 29:51
387 views
11

Ann Nowe (Vrije Universiteit Brussel)
https://simons.berkeley.edu/talks/learning-automata-building-blocks-marl
Multi-Agent Reinforcement Learning and Bandit Learning

In this talk I will show that Learning Automata (LA), and more precisely Reward in Action update schemes are interesting building blocks for Multi-agent RL, both in bandit settings as well as stateful RL. Based on the theorem of Narendra and Wheeler we have convergence guarantees in n-person non-zero sum games. However, LA have also shown to be robust in more relaxed settings, such as queueing systems, where updates happen asynchronously and the feedback sent to the agents is delayed.



Other Videos By Simons Institute for the Theory of Computing


2022-05-05Multi-Player Bandits With No Collisions
2022-05-05What Does Machine Learning Offer Game Theory (And Vice Versa)?
2022-05-05Variants and Invariants in No-Regret Algorithms
2022-05-05When Is Offline Two-Player Zero-Sum Markov Game Solvable?
2022-05-05General Game-Theoretic Multiagent Reinforcement Learning
2022-05-05Kernelized Multiplicative Weights for 0/1-Polyhedral Games:...
2022-05-04Multi-Agent Reinforcement Learning Towards Zero-Shot Communication
2022-05-04Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
2022-05-04Independent Learning in Stochastic Games
2022-05-04On Rewards in Multi-Agent Systems
2022-05-04Learning Automata as Building Blocks for MARL
2022-05-04Efficient Error Correction in Neutral Atoms via Erasure Conversion | Quantum Colloquium
2022-05-04Multi-Agent Reinforcement Learning in the High Population Regime
2022-05-04A Regret Minimization Approach to Mutli-Agent Control and RL
2022-05-03The Complexity of Markov Equilibrium in Stochastic Games
2022-05-03The Complexity of Infinite-Horizon General-Sum Stochastic Games: Turn-Based and Simultaneous Play
2022-05-03Policy Gradients in General-Sum Dynamic Games: When Do They Even Converge?
2022-05-03No-Regret Learning in Time-Varying Zero-Sum Games
2022-05-03What is the Statistical Complexity of Reinforcement Learning?
2022-05-03V-Learning: Simple, Efficient, Decentralized Algorithm for Multiagent RL
2022-05-02"Calibeating": Beating Forecasters at Their Own Game


Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Multi-Agent Reinforcement Learning and Bandit Learning
Ann Nowe