Deep q-learning for nash equilibria: nash-dqn
WebHere, we develop a new data-efficient Deep-Q-learning methodology for model-free learning of Nash equilibria for general-sum stochastic games. The algorithm uses a locally linear-quadratic expansion of the stochastic game, which leads to analytically solvable optimal actions. WebJul 18, 2024 · We propose (a) Nash-DQN algorithm, which integrates the deep learning techniques from single DQN into the classic Nash Q-learning algorithm for solving tabular Markov games; (b) Nash-DQN-Exploiter algorithm, which additionally adopts an exploiter to guide the exploration of the main agent.
Deep q-learning for nash equilibria: nash-dqn
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WebHardworking and passionate data scientist bringing four years of expertise in Machine Learning, Natural Language Processing (NLP), Reinforcement Learning and Deep Learning. Skilled multitasker with excellent communication and organizational skills. Quick learner and ability to demonstrated ability to grasp difficult and emerging … WebJan 1, 2024 · A Theoretical Analysis of Deep Q-Learning. Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well …
WebThis repository contains the code for the Nash-DQN algorithm for general-sum multi-agent reinforcement learning. The associated paper "Deep Q-Learning for Nash Equilibria: Nash-DQN" can be found at … WebAn approach called Nash-Q [9, 6, 8] has been proposed for learning the game structure and the agents’ strategies (to a fixed point called Nash equilibrium where no agent can improve its expected payoff by deviating to a different strategy). Nash-Q converges if a unique Nash equilibrium exists, but generally there are multiple Nash equilibria ...
WebApr 21, 2024 · Nash Q-Learning As a result, we define a term called the Nash Q-Value: Very similar to its single-agent counterpart, the Nash Q-Value represents an agent’s … WebFor computational efficiency the network outputs the Q values for all actions of a given state in one forward pass. This technique is called Deep Q Network (DQN). While the use of …
Webanalysis of DQN, we also quantify the difference between the policies obtained by Minimax-DQN and the Nash equilibrium of the Markov game in terms of both the algorithmic and …
WebApr 23, 2024 · Here, we develop a new data efficient Deep-Q-learning methodology for model-free learning of Nash equilibria for general-sum stochastic games. The … is aim long term or short termWebSep 1, 2024 · We explore the use of policy approximation for reducing the computational cost of learning Nash equilibria in multi-agent reinforcement learning scenarios. We propose a new algorithm for zero-sum stochastic games in which each agent simultaneously learns a Nash policy and an entropy-regularized policy. The two policies help each other … olg address torontoWebApr 22, 2024 · Here, we develop a new data efficient Deep-Q-learning methodology for model-free learning of Nash equilibria for general-sum stochastic games. The … olg address ontarioWebExisting reinforcement learning algorithms, however, are often restricted to zero-sum games, and are applicable only in small state-action spaces or other simplified settings. … olga dystheWebReviewer 2 Summary. The paper presents a reduction of supervised learning using game theory ideas that interestingly avoids duality. The authors drive the rationale about the connection between convex learning and two-person zero-sum games in a very clear way describing current pitfalls in learning problems and connecting these problems to finding … olga dearborn heights miWebEnter the email address you signed up with and we'll email you a reset link. olga easy does it size chartWebMar 24, 2024 · [17] Xu C., Liu Q., Huang T., Resilient penalty function method for distributed constrained optimization under byzantine attack, Information Sciences 596 (2024) 362 – 379. Google Scholar [18] Shi C.-X., Yang G.-H., Distributed nash equilibrium computation in aggregative games: An event-triggered algorithm, Information Sciences 489 (2024) … olga dysthe 2003