I recently created 4 agents to trade NQ futures and I have successfully integrated them with Interactive Brokers. Categories: Reinforcement Learning. To train the reinforcement learning agent, you. During learning, we try to learn the value of applying particular actions in particular states. An artificial agent, implemented as a distinct actor based on. Our key idea is to assign an RL agent to intersections. Reinforcement Learning with R. MARL achieves the cooperation (sometimes competition) of agents by modeling each agent as an RL agent and setting their reward. The dynamics of reinforcement learning in. The goal of this work is to study multi-agent sys-tems using deep reinforcement learning (DRL). This part (finally!) focus on reinforcement learning (RL) and multi-agent RL. This blog post is a brief tutorial on multi-agent RL and how we designed for it in RLlib. Textured, rubber grips absorb recoil and enhance user control. Reinforcement learning is also used in self-driving cars, in trading and finance to predict stock prices, and in healthcare for diagnosing rare diseases. In this section we extend the theory of MDPs to the case of multiple deci-sion makers in the same environment. I have selected some relatively important papers with open source code and categorized them by time and method. What is Multiagent Reinforcement Learning (MARL)?. Multi-Agent Reinforcement Learning (MARL) papers with code - GitHub - TimeBreaker/MARL-papers-with-code: Multi-Agent Reinforcement Learning (MARL) papers . Aug 26, 2020 · Understanding Multi-Agent Reinforcement Learning This concept comes from the fact that most agents don’t exist alone. Models simulation environemnts as agent-enttity graphs. ICML, 1994. As it can learn the actions that result in eventual success in an unseen environment without the help of a supervisor, reinforcement learning is a very powerful algorithm. For MARL cooperation tasks, the simplest idea is to directly apply single-agent reinforcement learning methods to multi-agent systems. GitHub is where people build software. However, most of them share similar behavior and property. A suite of test scenarios for multi-agent reinforcement learning. Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. Oct 26, 2022 · Mava is a library for building multi-agent reinforcement learning (MARL) systems. py You can also launch the training regularly as python test_agent. The RL makes an agent enable to progressively learn a sequence of actions to achieve the desired goals [ref4]. There are many different techniques for model-free reinforcement learning, all with the same basis: We execute many different episodes of the problem we want to solve, and from that we learn a policy. Jun 16, 2020 · The environment represents the problem on a 3x3 matrix where a 0 represents an empty slot, a 1 represents a play by player 1, and a 2 represents a play by player 2. We are looking for a versatile, enthusiastic engineer capable of multi-tasking to join our team. 99 on a SGI Silicon Graphics Indy development machine which. The dynamics of reinforcement learning in cooperative multiagent systems by Claus C, Boutilier C. Learning transferable cooperative behavior in multi-agent teams. Multi-agent reinforcement learning The field of multi-agent reinforcement learning has become quite vast, and there are several algorithms for solving them. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a specific focus on deep. Learning transferable cooperative behavior in multi-agent teams. Our key idea is to assign an RL agent to intersections. Its study combines the pursuit of finding ideal algorithms that maximize rewards with a more sociological set of concepts. This a generated list, with all the repos from the awesome lists, containing the topic reinforcement-learning. Oct 26, 2022 · Mava is a library for building multi-agent reinforcement learning (MARL) systems. The earliest precedent on multi-agent deep reinforcement learning (MADRL) is from Tampuu et al. Multi-agent Reinforcement Learning flowchart using LaTeX and TikZ · GitHub Instantly share code, notes, and snippets. Paper 📃, ⌨️ Code-NeurComm. Uses GNN. Multi-Agent Reinforcement Learning for Adaptive Routing. Advantages of Policy Gradient Method. Specically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. “ 10; Problem instances for “Multi-Agent Deep Reinforcement Learning based Real-time Planning Approach for 3. In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL). To train the reinforcement learning agent, you. MATLAB , and Salesforce Einstein integrations. Each RL agent operates as a router agent and is responsible for provid-ing routing instructions to approaching vehicles. Mava provides useful components, abstractions, utilities and tools for MARL and allows for simple scaling for multi-process system training and execution while providing a high level of flexibility and composability. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. Multi-agent reinforcement learning is closely related to game theory and especially repeated games, as well as multi-agent systems. The proposed multi-agent A2C is compared against independent A2C and independent Q-learning algorithms, in both a large synthetic traffic grid and a large real-world traffic. Multi-agent reinforcement learning for networked system control. Mava is a library for building multi-agent reinforcement learning (MARL) systems. In general, decision-making in multi-agent settings is intractable due to the exponential growth of the problem size with increasing number of agents. For instance, in some multi-agent reinforcement learning (MARL) applications, agents may not have perfect state information (e. One of the simplest approaches is. monitor = trainingProgressMonitor (); Create a MonitorLogger object using rlDataLogger. 论文链接： 代码链接： GitHub - PKU-MARL/Multi-Agent-Transformer 背景 现有大多数MARL方法都基于CTDE范式，但这些方法都不能很好的cover多智能体交互的全部复杂性，为此HAPPO提出multi-agent advantage decomposition定理如下 该定理证明了联合优势函数 A_\pi^ {i_ {1:n}} 可以分解为每个智能体 i_m 的优势函数 A_\pi^ {i_m} 之和，其中智能体 i_m 的优势函数 A_\pi^ {i_m} 需要基于前面所有智能体的动作 a^ {i_ {1:m-1}} ，这样就有效地将多智能体联合策略优化转换为序列策略优化过程，也就是按顺序依次优化每个智能体的策略，并可以保证性能单调提升。. by Hu, Junling, and Michael P. Permissive License, Build not available. Uses GNN. 论文链接： 代码链接： GitHub - PKU-MARL/Multi-Agent-Transformer 背景 现有大多数MARL方法都基于CTDE范式，但这些方法都不能很好的cover多智能体交互的全部复杂性，为此HAPPO提出multi-agent advantage decomposition定理如下 该定理证明了联合优势函数 A_\pi^ {i_ {1:n}} 可以分解为每个智能体 i_m 的优势函数 A_\pi^ {i_m} 之和，其中智能体 i_m 的优势函数 A_\pi^. Uses GNN. Multi-agent reinforcement learning for networked system control. The RL agent learns to perform. Uses GNN. Deep Q-learning (DQN) for Multi-agent Reinforcement Learning (RL) - GitHub - mohammadasghari/dqn-multi-agent-rl: Deep Q-learning (DQN) for Multi-agent . AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents. Dec 07, 2021 · Pytorch implements multi - agent reinforcement learning algorithms including IQL, QMIX, VDN, COMA, QTRAN (QTRAN-Base and QTRAN-Alt), MAVEN, CommNet, DYMA-Cl, and G2ANet, which are among the most. Deep Reinforcement Learning in Action teaches you how to program agents that learn and improve based on. Multi-Agent Reinforcement Learning for Adaptive Routing. Models simulation environemnts as agent-enttity graphs. There are many different techniques for model-free reinforcement learning, all with the same basis: We execute many different episodes of the problem we want to solve, and from that we learn a policy. Research problems include scalable learning of coordinated agent policies and inter-agent communication; reasoning about the behaviours, goals, and composition of other agents from limited observations; and sample-efficient learning based on intrinsic motivation, curriculum learning, causal inference, and representation learning. Deep Q-learning (DQN) for Multi-agent Reinforcement Learning (RL) - GitHub - mohammadasghari/dqn-multi-agent-rl: Deep Q-learning (DQN) for Multi-agent . In the Wi-Fi. multiagent reinforcement learning in markov games. Basic Formalisms & Algorithms. Recent advances in multi-agent reinforcement learning have largely limited training one model from scratch for every new task. Coordination in Multiagent Reinforcement Learning: A Bayesian Approach. However, most of them share similar behavior and property. Search ACM Digital Library. “ 10; Problem instances for “Multi-Agent Deep Reinforcement Learning based Real-time Planning Approach for 3. multiagent reinforcement learning in markov games. multi agent reinforcement learning github ci nq In this article, we exploredthe application of TensorFlow-Agentsto Multi-Agent Reinforcement Learningtasks, namely for the MultiCarRacing-v0 environment. due to inaccurate measurement . Log In My Account xm. The RL makes an agent enable to progressively learn a sequence of actions to achieve the desired goals. Related Topics: Stargazers: Stargazers:. In multi-agent reinforcement learning problems, there are usually tons of thousand agents cooperate with each other in the environment. Jun 16, 2020 · The environment represents the problem on a 3x3 matrix where a 0 represents an empty slot, a 1 represents a play by player 1, and a 2 represents a play by player 2. Setup The easiest way to install MADRL and its dependencies is to perform a recursive clone of this repository. MultiAgent, Reinforcement learning, RoboCup Rescue Simulator. Recent advances in multi-agent reinforcement learning have largely limited training one model from scratch for every new task. When a vehicle reaches an intersection, it submits a routing query to the RL agent. I recently created 4 agents to trade NQ futures and I have successfully integrated them with Interactive Brokers. Models simulation environemnts as agent-enttity graphs. Models simulation environemnts as agent-enttity graphs. This is WIP. For MARL papers and MARL resources, please refer to Multi Agent Reinforcement Learning papersand MARL Resources Collection. An open source framework that provides a simple, universal API for. Markov games as a framework for multi-agent reinforcement learning by Littman, Michael L. Uses GNN. Originating in the Research Team at InstaDeep. multiagent-particle-environment: follow install instructions in https://github. ICML, 1998. Such advice is commonly given in the form of state-action pairs. Originating in the Research Team at InstaDeep. To support different trading tasks, we need to train multiple agents using various environments. When the agent number increases largely, the learning . For MARL papers with code and MARL resources, please refer to MARL Papers with Code and MARL Resources Collection. What is CityFlow? CityFlow is a new designed open-source traffic simulator, which is much faster than SUMO (Simulation of Urban Mobility). In multi-agent reinforcement learning problems, there are usually tons of thousand agents cooperate with each other in the environment. Please note that some processing of your personal data may not require your consent, but you have a right to object to such processing. Secondly, we analyze the cooperative and competitive behaviors between agents by adjusting the reward functions for each agent, which overcomes the limitation of single-agent reinforcement learning algorithms. The dynamics of reinforcement learning in cooperative multiagent systems by Claus C, Boutilier C. To train the reinforcement learning agent, you. February 2017 the source code of the N64 version was sold on eBay for $2551. With this in mind, our focus is on multi-agent reinforcement learning methods which allow. Abstract:Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e. LEFT Let’s set the rewards now, 1. SJTU Multi-Agent Reinforcement Learning Tutorial [Website]. The dynamics of reinforcement learning in. Reinforcement learning is the training of machine learning models to make a sequence of decisions. Models simulation environemnts as agent-enttity graphs. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. In this article, we propose a method to model multi-stock trading process according to reinforcement learning theory and implement our trading agents based on two popular actor-critic algorithms: A2C and PPO. Paper 📃, ⌨️ Code-NeurComm. monitor = trainingProgressMonitor (); Create a MonitorLogger object using rlDataLogger. In Proceedings of the 18th International Conference on. The algorithm (agent) evaluates a current situation (state), takes an action, and receives feedback (reward) from the environment after each act. The algorithm (agent) evaluates a current situation (state), takes an action, and receives feedback (reward) from the environment after each act. In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. The basic idea of n-step reinforcement learning is that we do not update the Q-value immediately after executing an action: we wait n steps and update it based on the n-step return. This makes them look a lot more like a real-life group of people trying their best to coordinate themselves. Learning with Opponent-Learning Awareness. Multi-Agent RL is bringing multiple single-agent together which can still retain their individual actions and rewards or have joint actions and rewards. GitHub; Instagram; Multi Agent reinforcement learning 3 minute read Understanding Multi-Agent Reinforcement Learning. This allows them to select one action from each branch at the same time step and then do both. , no unsafe states are ever visited) during the learning Therefore, we present two shielding approaches for safe MARL. Such advice is commonly given in the form of state-action pairs. GitHub is where people build software. Taking fairness into multi-agent learning could help multi-agent systems become both efficient and stable. GitHub is where people build software. Log In My Account xm. Oct 26, 2022 · Mava is a library for building multi-agent reinforcement learning (MARL) systems. However, most of them share similar behavior and property. Learning transferable cooperative behavior in multi-agent teams. It is an interdisciplinary domain with a long history that includes game theory, machine learning, stochastic control, psychology, and optimisation. com%2fTimeBreaker%2fMulti-Agent-Reinforcement-Learning-papers/RK=2/RS=8opACVWmW4qG9YNi4oj1MQylXyY-" referrerpolicy="origin" target="_blank">See full list on github. There are many different techniques for model-free reinforcement learning, all with the same basis: We execute many different episodes of the problem we want to solve, and from that we learn a policy. Learning to cooperate is crucially important in multi-agent environments. Each RL agent operates as a router agent and is responsible for provid-ing routing instructions to approaching vehicles. With this in mind, our focus is on multi-agent reinforcement learning methods which allow for automatically acquiring. However, policy gradient methods can be used for such cases. The goal of the agent is to maximize its cumulative expected reward. Mava is a library for building multi-agent reinforcement learning (MARL) systems. ment learning (Deep RL) is an emerging machine learning technology that can solve multi-step optimal control problems. Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. multi-agent reinforcement learning model for addressing this prob-lem. The dynamics of reinforcement learning in cooperative multiagent systems by Claus C, Boutilier C. This may sound like a bad. Its study combines the pursuit of finding ideal algorithms that maximize rewards with a more sociological set of concepts. The environment doesn’t use any external data. Balancing exploitation and exploration is one of the key challenges in Reinforcement Learning and an issue that doesn't arise at all in pure forms of supervised and unsupervised learning. To tackle these difficulties, we propose FEN, a novel hierarchical reinforcement learning model. Reinforcement learning is based on a delayed and cumulative reward system. Core principles of reinforcement learning. In this system, an agent reconciles an action that influences a state change of the environment. Multi-agent reinforcement learning for networked system control. Recent advances in multi-agent reinforcement learning have largely limited training one model from scratch for every new task. Feudal Multi-Agent Deep Reinforcement. Learning transferable cooperative behavior in multi-agent teams. IEEE Transactions on Systems, Man. A fixed, ramp front sight and a fixed, groove rear sight. We are just going to look at how we can extend the lessons leant in the first part of these notes to work for stochastic games, which are generalisations of extensive form games. Setup The easiest way to install MADRL and its dependencies is to perform a recursive clone of this repository. Research Direction. Multi-agent adversarial inverse reinforcement learning. Uses GNN. by Hu, Junling, and Michael P. Paper 📃, ⌨️ Code-Agent-Entity-Graph. Multi-agent Reinforcement Learning flowchart using LaTeX and TikZ Raw marl. In multi-agent reinforcement learning problems, there are usually tons of thousand agents cooperate with each other in the environment. LEFT Let’s set the rewards now, 1. , no unsafe states are ever visited) during the learning Therefore, we present two shielding approaches for safe MARL. MultiAgent, Reinforcement learning, RoboCup Rescue Simulator. Aug 7, 2021 | 20 min | Omri Kaduri Reinforcement-Learning Multi-Agent An intuitive high-level overview of the connection between AI planning theory to current Reinforcement Learning research for multi-agent systems. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. Mava provides useful components, abstractions, utilities and tools for MARL and allows for simple scaling for multi-process system training and execution while providing a high level of flexibility and composability. FinRL-Meta: Data-Driven Deep Reinforcement Learning in Quantitative Finance. An open source framework that provides a simple, universal API for. In this paper, they propose a role-oriented multi-agent reinforcement learning framework, called ROMA,which implicitly introduce two regularizers in the training procedure so that the. Uses GNN. However, the code is quite rigidly tied to the single-agent view, which is explained by the extrinsically motivatedagent in the diagram below. Oct 26, 2022 · Mava is a library for building multi-agent reinforcement learning (MARL) systems. One of the challenges that arise in reinforcement learning, and not in other kinds of learning, is the trade-o between exploration and exploitation. This blog post is a brief tutorial on multi-agent RL and how we designed for it in RLlib. facebook video downloader apk, bareback escorts
Paper 📃, ⌨️ Code-NeurComm. . Multi agent reinforcement learning github
Apr 17, 2020 · This makes them look a lot more like a real-life group of people trying their best to coordinate themselves. Multi-agent reinforcement learning for networked system control. reinforcement and imitation learning to solve multi-robot path planning and utilizes a centralized method to generate training data Robust Image-Based Landing Control of a Quadrotor on an. Zhang at SJTU 2018. Now, the goal is to learn a path from Start cell represented by S to Goal Cell represented by G without going into the blocked cell X. Attention-Modulated Reinforcement Learning We obtained two quantitative trial-by-trial measures of participants’ attention to each dimension. The dynamics of reinforcement learning in. This limitation occurs due to the restriction of the model. Decentralized Learning, Pre-defined All-to-all Communication. Building such models by hand is recognized as a very challenging task. Uses GNN. git clone --recursive git@github. We are looking for a versatile, enthusiastic engineer capable of multi-tasking to join our team. Expand 24 Highly Influential PDF View 6 excerpts, references background and methods. To support different trading tasks, we need to train multiple agents using various environments. Reinforcement learning (RL) approaches provide us with a more seamless framework for decision making [Bacoyannis et al. As JALs offer good coordination results but suffer from the curse of dimensionality, a new kind of distributed multi-agent reinforcement learning algorithm, called the ThMLA-JAG (Three Model Learning Architecture based on Joint Action Generalization) method, is proposed here. In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL). framework reinforcement-learning openai-gym pytorch policy-gradient multiagent-reinforcement-learning multi-agent-reinforcement-learning marl sqddpg shapley-q-value multi-agent-rl. This limitation occurs due to the restriction of the model. txt run. To train the reinforcement learning agent, you. For advanced research topics like reinforcement learning, sparse coding, or GAN research, it may be To use multiple optimizers (optionally with learning rate schedulers), return two or more optimizers from configure_optimizers(). Welcome to another part of my step-by-step reinforcement learning tutorial with gym and TensorFlow 2. Multi agent reinforcement learning github. Let's take a deep dive into reinforcement learning. ment learning (Deep RL) is an emerging machine learning technology that can solve multi-step optimal control problems. SMAC is a decentralized micromanagement scenario for StarCraft II. Updated: April. kandi ratings - Low support, No Bugs, No Vulnerabilities. The agent gener- ates a routing response based on the vehicle’s final destinationand. Mar 28, 2022 · Multi-agent reinforcement learning (MARL) is a technique introducing reinforcement learning (RL) into the multi-agent system, which gives agents intelligent performance [ 6]. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards. ICML, 1994. Its study combines the pursuit of finding ideal. Monte Carlo Tree Search (MTCS) is a name for a set of algorithms all based around the same idea. However, I am facing a problem in running the learning for my agents. If T is the termination step and t + n ≥ T, then we just use the full reward. TF-Agents is a framework for designing and experimenting with RL algorithms. md install_sc2. Multi-agent reinforcement learning for networked system control. This is a challenging task for current state-of-the-art multi-agent reinforcement algorithms that are designed to either maximize the global reward of the team or the individual local rewards. Multi-Agent Reinforcement Learning Problem Definition and Research Motivation In many real-world scenarios, people need to control multiple agents that exist at the same time to. deep multi agent reinforcement learning tutorial book for intermediate - Issues · seolhokim/Deep-Multi-Agent-Reinforcement-Learning. In general, there are two types of multi-agent systems: independent and cooperative systems. AnyTrading is a collection of OpenAI Gym environments for reinforcement learning-based trading algorithms. Recent advances in multi-agent reinforcement learning have largely limited training one model from scratch for every new task. Paper 📃, ⌨️ Code-Agent-Entity-Graph. Recent advances in multi-agent reinforcement learning have largely limited training one model from scratch for every new task. Despite being far from a mathematically perfect cycle, a system like this is probably much more adaptive. It supports both deep Q learning and multi-agent deep Q learning that . The algorithm (agent) evaluates a current situation (state), takes an action, and receives feedback (reward) from the environment after each act. Uses GNN. More than 100 million people use GitHub to discover, fork, and contribute to over 330 million projects. Abstract:Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e. Once you've installed Ray and RLlib with pip install ray[rllib], you can train your first RL agent with a single command in the command line. Zhang at SJTU 2018. Paper 📃, ⌨️ Code-Agent-Entity-Graph. Oct 26, 2022 · Mava is a library for building multi-agent reinforcement learning (MARL) systems. I worked on developing a generative model for InfoRL to. com%2fTimeBreaker%2fMulti-Agent-Reinforcement-Learning-papers/RK=2/RS=8opACVWmW4qG9YNi4oj1MQylXyY-" referrerpolicy="origin" target="_blank">See full list on github. SMAC is a decentralized micromanagement scenario for StarCraft II. Later, we look at solving single-agent MDPs in a model-free manner and multi-agent MDPs using MCTS. Paper 📃, ⌨️ Code-Agent-Entity-Graph. The episode ends when either agent loses all five lives, or after 3000 timesteps has passed. Let's take a deep dive into reinforcement learning. During learning, we try to learn the value of applying particular actions in particular states. Reinforcement learning (RL) is an effective solution as a famous machine- learning tool for learning in multi - agent systems, which is employed to. SJTU Multi-Agent Reinforcement Learning Tutorial [Website]. Create a trainingProgressMonitor object. Paper 📃, ⌨️ Code-NeurComm. Setup The easiest way to install MADRL and its dependencies is to perform a recursive clone of this repository. by Hu, Junling, and Michael P. Dec 07, 2021 · Pytorch implements multi - agent reinforcement learning algorithms including IQL, QMIX, VDN, COMA, QTRAN (QTRAN-Base and QTRAN-Alt), MAVEN, CommNet, DYMA-Cl, and G2ANet, which are among the most. Uses GNN. Tremendous potential for using Machine learning in unreal engine 4, recently published a Q - learning algorithm in the marketplace demonstrating how an AI character using reinforcement learning can solve a match to sample puzzle. Multi-agent Reinforcement Learning flowchart using LaTeX and TikZ Raw marl. The agent learns to achieve a goal in an uncertain, potentially complex environment. Recent advances in multi-agent reinforcement learning have largely limited training one model from scratch for every new task. Multiagent reinforcement learning: theoretical framework and an algorithm. Despite being far from a mathematically perfect cycle, a system like this is probably much more adaptive. IEEE Transactions on Systems, Man. For MARL papers and MARL resources, please refer to Multi Agent Reinforcement Learning papersand MARL Resources Collection. Among many other deep learning techniques, Reinforcement Learning (RL) and its popularity have been on the rise. DOWN 3. Revolutionizing Trading with Reinforcement Learning AI: A Guide to Multi-Task Trading Hello, I am seeking help from experienced traders/programmers. The dynamics of reinforcement learning in. Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges Reinforcement learning (RL) has distinguished itself as a prominent learning method to augment payoffs • Li Deng, How deep reinforcement learning can help chatbots • Christopher Olah, colah. MARL achieves the cooperation (sometimes competition) of agents by modeling each agent as an RL agent and setting their reward. Topic: multi-agent-reinforcement-learning Goto Github Some thing interesting about multi-agent-reinforcement-learning. Mava provides useful components, abstractions, utilities and tools for MARL and allows for simple scaling for multi-process system training and execution while providing a high level of flexibility and composability. Uses GNN. This stock price data is from 2000–10–20 to 2020–09–04. io In the A comprehensive survey of multiagent rein- forcement learning. arXiv preprint arXiv:1509. In the previous blog posts, we saw Q-learning based algorithms like DQN and DRQNs where given a state we were finding the Q-values of the possible actions where the Q-values are the expected return for the episode we can get from that state if that action is selected. The dynamics of reinforcement learning in cooperative multiagent systems by Claus C, Boutilier C. . mecojo a mi hermana