/Resources 158 0 R >> But, multiagent environments are inherently non-stationary since the other agents are free to change their behavior as they also learn and adapt. /Type /Page /Type /Page This work has thus far only been applied to small games with enumerable state and action spaces. This learning protocol provably converges given certain restrictions on the stage games (deﬁned by Q-values) that arise during learning. We study online reinforcement learning in average-reward stochastic games (SGs). Some features of the site may not work correctly. Mean-Field Games, Evolutionary Games and Stochastic Games are having an impact in the new generation of reinforcement learning systems. /Book (Advances in Neural Information Processing Systems 30) /Description-Abstract (We study online reinforcement learning in average\055reward stochastic games \050SGs\051\056 An SG models a two\055player zero\055sum game in a Markov environment\054 where state transitions and one\055step payoffs are determined simultaneously by a learner and an adversary\056 We propose the \134textsc\173UCSG\175 algorithm that achieves a sublinear regret compared to the game value when competing with an arbitrary opponent\056 This result improves previous ones under the same setting\056 The regret bound has a dependency on the \134textit\173diameter\175\054 which is an intrinsic value related to the mixing property of SGs\056 Slightly extended\054 \134textsc\173UCSG\175 finds an \044\134varepsilon\044\055maximin stationary policy with a sample complexity of \044\134tilde\173\134mathcal\173O\175\175\134left\050\134text\173poly\175\0501\057\134varepsilon\051\134right\051\044\054 where \044\134varepsilon\044 is the error parameter\056 To the best of our knowledge\054 this extended result is the first in the average\055reward setting\056 In the analysis\054 we develop Markov chain\047s perturbation bounds for mean first passage times and techniques to deal with non\055stationary opponents\054 which may be of interest in their own right\056) /Author (Chen\055Yu Wei\054 Yi\055Te Hong\054 Chi\055Jen Lu) Definition 2 (Learning in stochastic games) A learning problem arises when an agent does not know the reward function or the state transition probabilities. LMRL2 is designed to overcome a pathology called relative overgeneralization, and to do so while still performing well in games with stochastic transitions, stochastic rewards, and miscoordination. /Resources 155 0 R Compared with evolutionary biology, reinforcement learning is more suitable for guiding individual decision making. /Type /Page Stochastic policies are in general more robust than deterministic policies in two major problem areas. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 5 0 obj endobj /Parent 1 0 R 3:13. 3 0 obj Its players learn independently through environmental feedback. In this type of games, we propose two multi-agent reinforcement learning algorithms to solve the problem of learning when each learning agent has only minimum knowledge about the underlying game and the other learning agents. Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel. /Contents 69 0 R Title: A REINFORCEMENT LEARNING ALGORITHM FOR COORDINATION IN STOCHASTIC GAMES. Browse our catalogue of tasks and access state-of-the-art solutions. towardsdatascience.com. /ArtBox [ 0 0 612 792 ] << >> Stochastic games can generally model the interactions between multiple agents in an environment. *���l�M�+�0��q��Qb��Lѡݕu�c_��vs�����i�]��O�!$�Ù7��������)Z�]umCzʣ�e'{�C1��0җ���8��Jm��,�g�*��͡�r�呇���Y��v�?�;ߕC��LY�W�S=Mٞ������Ul�i����L� /Created (2017) /ModDate (D\07220180212220758\05508\04700\047) The resulting multi-agent reinforcement learning (MARL) framework assumes a group of autonomous agents that share a common environment in which the agents choose actions independently and interact with each other [5] to reach an equilibrium. Stochastic games provide a framework for interactions among multi-agents and enable a myriad of applications. relevant results from game theory towards multiagent reinforcement learning. towardsdatascience.com. %PDF-1.3 endobj the problem of satisfying an LTL formula in a stochastic game, can be solved via model-free reinforcement learning when the environment is completely unknown. /Contents 153 0 R Reinforcement learn-ing [Sutton and Barto, 1998] has been successful at ﬁnd-ing optimal control policies in the MDP framework, and has /Parent 1 0 R 1. One widely adopted framework to address multi-agent systems is via Stochastic Games (SG). /Type /Page Stochastic games, first studied in the game theory community, are a natural extension of MDPs to include multiple agents. >> I. 1.1 Reinforcement Learning 1 1.2 Deep Learning 1 1.3 Deep Reinforcement Learning 2 1.4 What to Learn, What to Approximate 3 1.5 Optimizing Stochastic Policies 5 1.6 Contributions of This Thesis 6 2background8 2.1 Markov Decision Processes 8 2.2 The Episodic Reinforcement Learning Problem 8 2.3 Partially Observed Problems 9 2.4 Policies 10 Google Scholar; Marilyn A. Walker. In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation. endobj /Type (Conference Proceedings) endobj /ArtBox [ 0 0 612 792 ] �C��.g��B���'j�Z�([�Qf*^mOh���ʄy��ru��'__?��)榡V�]]߮��a�ǫ$��<6����M�]SWM���8 Mulitagent Reinforcement Learning in Stochastic Games with Continuous Action Spaces Albert Xin Jiang Department of Computer Science, University of British Columbia jiang@cs.ubc.ca April 27, 2004 Abstract We investigate the learning problem in stochastic games with continu-ous action spaces. High dimensional & stochastic continuous action spaces policy ˇ ˚ ( s framework of general- sum games! Present a distributed Q-Learning approach for independently learning agents in an environment deﬁned by a probabilistic transition function can the. Are also due to the noise in the environment, optimistic learners overestimate real Q I values equilibrium if! 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