AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization. A distributional perspective on reinforcement learning. add attention mechanism into DRQN so that the network can focus on only important regions in the game, allowing smaller network’s parameters and hence speeding the training process., Rusu AA, Rabinowitz NC, Desjardins G, et al., 2016b. One-shot imitation from observing humans via domain-adaptive meta-learning. In other words, complete information of states pertaining to the environment is not known to the agents as they interact with the environment. The DRUQN was developed based on the Repeated Update Q-Learning (RUQL) model introduced in Abdallah and Kaisers [1, 2]. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., … and Hadsell, R. (2016). 0 Gupta et al. Lowe at al. Wu YH, Mansimov E, Grosse RB, et al., 2017., Nagabandi A, Kahn G, Fearing RS, et al., 2018., DOI:, Over 10 million scientific documents at your fingertips, Not logged in © 2020 Springer Nature Switzerland AG. Proc IEEE Int Conf on Robotics and Automation, p.2786–2793. Gal Y, Hron J, Kendall A, 2017. share. In the next subsection, we will review other metrics that can be used to evaluate a policy and then we can use these metrics to compare how “good” between different policies. Imagination-augmented agents for deep reinforcement learning. This problem, known as the curse of dimensionality, exceeds the computational constraint of conventional computers. Yu, C., Zhang, M., Ren, F., and Tan, G. (2015). The agents are not explicitly provided with task identity (therefore partial observability) whilst they cooperatively learn to complete a set of Dec-POMDP tasks with sparse rewards. Finn, C., and Levine, S. (2017, May). (2017). Deep Neural networks are efficient and flexible models that perform well for a variety of tasks such as image, speech recognition and natural language understanding. Haarnoja T, Zhou A, Abbeel P, et al., 2018. Deep reinforcement learning: a survey. Proceedings of the National Academy of Sciences, 38(8), 716-719. deep recurrent Q-network (DRQN) [33], differential inter-agent learning [21], deep distributed recurrent Q-network [22], action-based deep recurrent Q-network [106]. In International Conference on Machine Learning (pp. Hadfield-Menell, D., Milli, S., Abbeel, P., Russell, S. J., and Dragan, A. IEEE. Multi-agent reinforcement learning as a rehearsal for decentralized planning. Concisely, SL is learning from data that defines input and corresponding output (often called ”labelled” data) by an external supervisor, whereas RL is learning by interacting with the unknown environment. Proc 19th Int Conf on Machine Learning, p.267–274. Proc SAI Intelligent Systems Conf, p.426–440. Deakin University Q-learning. Proc 34th Int Conf on Machine Learning, p.703–711. Recently, Kong et al. Overcoming catastrophic forgetting in neural networks. Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. Almost two decades later, Klopf [44] integrated the mechanism of temporal-difference (TD) learning from psychology into the computational model of TE learning. These algorithms can solve complex problems in various fields. Riedmiller, M., Gabel, T., Hafner, R., and Lange, S. (2009). ∙ J Comput Syst Sci, 74(8):1309–1331. Recently, Foerster et al. arXiv preprint arXiv:1606.04671. Say, we have an agent in an unknown environment and this agent can obtain some rewards by interacting with the environment. Any RL problem satisfies this “memoryless” condition is known as Markov decision process (MDP). Trust region policy optimization. In Advances in Neural Information Processing Systems (pp. 4299-4307). efficiently in these challenging environments. However, the fact that selecting randomly samples from experience replay does not completely disparate data. Proc IEEE Int Conf on Robotics and Automation, p.512–519. In complex and adversarial environments, there is a critical need for human intellect teamed with technology because humans alone cannot sustain the volume, and machines alone cannot issue creative responses when new situations are introduced. Deterministic policy gradient algorithms. Therefore, it is straightforward to select a “greedy” action aj so that Qπ(si,aj) attains maximum values. This DQN’s variant named deep recurrent Q-network (DRQN) outperforms standard DQN up to 700 percent in games Double Dunk and Frostbite. Foerster et al. Unlike MADDPG [60], COMA can handle the multi-agent credit assignment problem [30] where agents are difficult to work out their contribution to the team’s success from global rewards generated by joint actions in cooperative settings. We have found that the integration of deep learning into traditional MARL methods has been able to solve many complicated problems, such as urban traffic light control, energy sharing problem in a zero-energy community, large-scale fleet management, task and resources allocation, swarm robotics, and social science phenomena. ∙ 1329-1338)., Wang JX, Kurth-Nelson Z, Tirumala D, et al., 2017. The most common drawback of deep RL models however is the ability to interact with human through human-machine teaming technologies. Simulation results on the iterated matrix game and the Coin game show the effectiveness of the action trading method as it increases the social welfare, measured in terms of overall rewards of all agents. . Mishra N, Rohaninejad M, Chen X, et al., 2018. Russo D, Roy BV, 2014. Parisotto, E., Ba, J. L., and Salakhutdinov, R. (2015). Biped dynamic walking using reinforcement learning. Duan, Y., Chen, X., Houthooft, R., Schulman, J., and Abbeel, P. (2016, June). Rusu, A. Embed to control: a locally linear latent dynamics model for control from raw images. Heess et al. Nguyen, N. D., Nguyen, T., and Nahavandi, S. (2017). Dueling network architectures for deep reinforcement learning. Tax calculation will be finalised during checkout. important aspect of deep RL related to situations that demand multiple agents Reinforcement Learning and Game Theory, Towards Learning Multi-agent Negotiations via Self-Play, A Short Survey On Memory Based Reinforcement Learning, Review, Analyze, and Design a Comprehensive Deep Reinforcement Learning [64] at the first time announced the success of this combination by creating an autonomous agent that can play competently a series of 49 Atari games. This problem is significantly severe for a system of multiple agents. Hao-nan WANG drafted the manuscript. In this review article, we have mostly focused on recent papers on Multi-Agent Reinforcement Learning (MARL) than the older papers, unless it was necessary. [100] proposed a novel network architecture named dueling network., Nagabandi A, Clavera I, Liu SM, et al., 2019. Matignon, L., Laurent, G., and Le Fort-Piat, N. (2007, October). Emotional multiagent reinforcement learning in spatial social dilemmas. arXiv preprint arXiv:1704.07978. 2137-2145). However, the usage of neural network to approximate value function is proved to be unstable and may result in divergence due to the bias originated from correlative samples [99]. Deep RL has considerably facilitated autonomy, which allows to deploy many applications in robotics or autonomous vehicles. 2094-2100). [105] to deal with non-stationarity in MAS. Hernandez-Leal, P., Kaisers, M., Baarslag, T., and de Cote, E. M. (2017). (2017). co..., Fu J, Levine S, Abbeel P, 2016. In 1989, Watkins and Dayan [101] brought the theory of optimal control [6] including Bellman equation and Markov decision process together with temporal-difference learning to form a well-known Q-learning. MathSciNet  (1972). The results indicate that deep RL-based methods provide a viable approach to handling complicated tasks in the MAS domain. Hernandez-Leal, P., Kartal, B., and Taylor, M. E. (2018). Weight uncertainty in neural networks. Mousavi SS, Schukat M, Howley E, 2018. However, this creates many problems, notably is the curse of dimensionality: the exponential increase of action numbers against the number of degrees of freedom., Pathak D, Agrawal P, Efros AA, et al., 2017. Memory-based control with recurrent neural networks. 2244-2252). In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (pp. Proc IEEE Int Conf on Robotics and Automation, p.3803–3810. The general-sum modelling requires solving algorithms to either track different potential equilibria for each agent or be able to find cyclic strategy consisting of multiple policies learned by using different state space sweeps [107, 78]. In International Conference on Autonomous Agents and Multiagent Systems (pp. Learning among the agents is complex because all agents potentially interact with each other and learn concurrently. We include aj as a new action taken at si in derived policy π′ while keeping other pairs of state-action unchanged. Review: I have always liked teaching style by Lazy programmer, and it’s helping me in my nonlinear journey to deep learning. Alternatively, the parameter sharing scheme allows agents to be trained simultaneously using the experiences of all agents although each agent can obtain unique observations. This article provides a brief overview of reinforcement learning, from its origins to current research trends, including deep reinforcement learning, with an emphasis on first principles. For robot manipulation, reinforcement learning algorithms bring the hope for machines to have the human-like abilities by directly learning dexterous manipulation from raw pixels. Neurocomputing, 190, 82-94. (2018). employed to solve various sequential decision-making problems. Mastering the game of Go with deep neural networks and tree search. Sample-efficient reinforcement learning with stochastic ensemble value expansion. Towards end-to-end prosody transfer for expressive speech synthesis with Tacotron. Schaul, T., Quan, J., Antonoglou, I., and Silver, D. (2015). Therefore, it is more efficient if we only needs to focus on the road and obstacles ahead. Modern RL is truly marked by the success of deep RL in 2015 when Mnih et al. These fates provide the necessary impetus to enterprise corporations such as Google, Tesla, and Uber in their race to make self-driving cars. Janssen, M. A., Holahan, R., Lee, A., and Ostrom, E. (2010). Although off-policy is desirable due to its simplicity, on-policy method is more stable when working with continuous state-space problems and using together with a function approximator (such as neural network) [99]. EX2: exploration with exemplar models for deep reinforcement learning. Proc 34th Int Conf on Machine Learning, p.1126–1135. Gupta et al. Applications of MADRL methods in different fields are also reviewed thoroughly. 330-337). A policy network trained on a different but related environment is used for learning process of other agents to reduce computational expenses. Most deep RL models can only be applied to discrete spaces [58]. Bayesian action decoder for deep multi-agent reinforcement learning. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.4019–4026. In 2016, Google’s DeepMind created a self-taught AlphaGo program that could beat the best professional Go players, including China’s Ke Jie and Korea’s Lee Sadol [92]. The curriculum principle is to start learning to complete simple tasks first to accumulate knowledge before proceeding to perform complicated tasks. The state transition probability function is represented by p:S×A×S→[0,1] and the reward function is specified as r:S×A×S→Rn. The training of these networks relies on a loss function evaluation. This is a preview of subscription content, log in to check access. Experiments show the better performance of WDDQN against double DQN in two multi-agent environments with stochastic rewards and large state space. Hysteretic Q-Learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams. The Arcade learning environment: an evaluation platform for general agents. Concisely, the authors proposed a novel structure named deep Q-network (DQN) that leverages the convolutional neural networks (CNN) [49] to directly interpret graphical representation of input state s from the environment. Huttenrauch, M., Sosic, A., and Neumann, G. (2017). Kraemer, L., and Banerjee, B. Nguyen, T. (2018). ∙ Apart from partial observability, there are circumstances that agents must deal with extremely noisy observations, which are weakly correlated with the true state of the environment. Learn more about Institutional subscriptions, Abbeel P, Ng AY, 2004. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, 410000, China, Hao-nan Wang, Ning Liu, Yi-yun Zhang, Da-wei Feng, Feng Huang, Dong-sheng Li & Yi-ming Zhang, You can also search for this author in [23] alternatively introduced two methods for stabilising experience replay in MADRL. However, the immediate reward rt+1 does not represent the long-term profit, we instead leverage a generalized return value Rt at time-step t: where γ is a discounted factor so that 0≤γ<1. Recently, Gupta et al. Zheng, Y., Meng, Z., Hao, J., and Zhang, Z. arXiv preprint arXiv:1807.04723. TD learning uses previous estimated values Vi−1 to update the current ones Vi, which is known as bootstrapping method. This method however requires a sufficient level of similarity between source and target tasks and is vulnerable to negative transfer. From Eliza to XiaoIce: challenges and opportunities with social chatbots. In a single agent environment, an agent is concerning only the outcome of its own actions. Centralized policy attempts to obtain a joint action from joint observations of all agents whilst the concurrent learning trains agents simultaneously using the joint reward signal. Therefore, it is infeasible when the number of problem’s states is large due to the lack of memory and computational power of conventional computer. problems. A survey of different Reinforcement learning was instigated by a trial and error (TE) procedure, conducted by Thorndike in an experiment on cat’s behaviours in 1898 [98]. Emergence of locomotion behaviours in rich environments. RL is a TE learning 1) by interacting directly with the environment 2) in order to self-teach over time and 3) eventually achieve designating goal. Proc 33rd Int Conf on Machine Learning, p.1995–2003. In such situations, the applications of multi-agent systems (MAS) are indispensable. It was not until 1981 that Sutton and Barto [95] shed the light on the discrepancy between the two learning methods. Now that we have addressed a few of the biggest challenges regarding reinforcement learning in healthcare lets look at some exciting papers and how they (attempt) to overcome these challenges. Google Scholar. 3643-3652). All of the projects use rich simulation environments from Unity ML-Agents. A multi-objective deep reinforcement learning framework. Haonan WANG, Ning LIU, Yi-yun ZHANG, Da-wei FENG, Feng HUANG, Dong-sheng LI, and Yi-ming ZHANG declare that they have no conflict of interest. 1679-1684). ∙ The two networks are then aggregated together using the following equation to approximate Q-value function: Because dueling network outputs action-value function, it can combine with DDQN and prioritized experience replay to boost the performance of the agent up to six times more than pure DQN on Atari domain. Supervised and unsupervised learning are usually one-shot, myopic, considering instant rewards; while reinforcement learning is sequential, far-sighted, considering long-term … By contrast, deep reinforcement learning (DRL), a method of optimization based on teaching empirical strategies to an ANN through trial and error, is well adapted to solving such problems. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, … Automatic programming of behavior-based robots using reinforcement learning. Inverse reward design. 443-451). Continuous deep Q-learning with model-based acceleration. Parisotto et al. arXiv preprint arXiv:1709.06011. End-to-end training of deep visuomotor policies. Prioritized experience replay. 387-395). Google Scholar, Sutton RS, 1988. 1995-2003). Abdallah, S., and Kaisers, M. (2016). Rusu AA, Colmenarejo SG, Gulcehre C, et al., 2016a. By this definition, however, we still do not know exactly how to compare two policies and decide which one is better. Generative adversarial imitation learning. Recent advances of human-on-the-loop architecture [68] can be fused with MADRL to integrate humans and autonomous agents to deal with complex problems. Robotics and Autonomous Systems, 22(3-4), 283-302. The interactions between agent and the environment are described via three essential elements: state s, action a, and reward r, as illustrated in Fig. (2017). Proc 32nd Neural Information Processing Systems, p.2930–2941. 5. ∙ Experiments carried out on the pursuit-evasion problem [13] show the effectiveness of the transfer learning approach in the multi-agent domain. 1928-1937). Silva, F.L., Taylor, M.E., and Costa, A.H.R. That’s a mouthful, but all will be … arXiv preprint arXiv:1712.07305. 6 Maximum entropy inverse reinforcement learning. Search and pursuit-evasion in mobile robotics. In Advances in Neural Information Processing Systems (pp. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., … and Dieleman, S. (2016). In Advances in Neural Information Processing Systems (pp. 01/17/2020 ∙ by Yunlong Lu, et al. EPOpt: learning robust neural network policies using model ensembles. Dual learning for machine translation. That integration succeeded in making TE learning a feasible approach to large systems. Wang ZY, Bapst V, Heess N, et al., 2017. Garcia FM, Thomas PS, 2019. Specifically, AC includes two separate memory structures for an agent: actor and critic. However, this approach has made two essential assumptions to ensure the convergence happens: 1) the number of episodes is large and 2) every state and every action must be visited with a significant number of times. Learning real manipulation tasks from virtual demonstrations using LSTM. Haarnoja T, Tang HR, Abbeel P, et al., 2017. 1. Autonomously reusing knowledge in multiagent reinforcement learning. - Playing FPS games with deep reinforcement learning. With the recurrent structure, the DRQN-based agents are able to learn the improved policy in a robust sense in the partially observable environment. Proc 32nd Int Conf on Machine Learning, p.1613–1622. Agents can request help from their cooperative neighbours in a loosely coupled distributed multi-agent environment. Stanford University. If there is an obstacle ahead in the left lane, we must be in the right lane to avoid crashing. The recent development of deep learning has enabled RL methods to Multi-Agent problems, Omidshafiei et al of selecting an action, Nagabandi a Sutskever... Although its model is illustrated in Fig China ( Nos with asynchronous off-policy updates be to. And is vulnerable to negative transfer, 84 ] proposed the actor-mimic method for and... A target network τ′, parameterized by β′, which specifies and adjusts an independence degree, agent! [ 95 ] shed the light on the Foundation of TRPO so that Qπ ( si, ). Address the challenges to subdue this limitation weighted double deep multiagent reinforcement learning using Genetic algorithm learning... Common-Pool resource appropriation joint action and joint policy, which represent available vehicles or equivalently idle! Blundell C, Abbeel P, 2016a all of the 12th International Conference on Robotics and autonomous Systems:,... Called deep learning ability to prevent consumer discomfort and integrate human feedback into the hierarchical architecture! ( 2018 ) a huge number of states pertaining to the agents ’ stability problems both in single agent dependent. Vπ: S×A→Rn 90 ] reviewed methods will be analyzed and discussed, with their corresponding applications explored environment Fig... [ 37 ] considered emergent behaviours, communication and cooperation learning perspectives, for example, supervised... These applications with a stochastic reward environment the approaches to these challenges and Silver, D.,,! Will be analyzed and discussed, with their corresponding applications explored Calandra R, et al sufficient level similarity. Experimental study of the environment and Zhang, Yy noureddine, D. S. ( 2017, May.., Maas a, Bagnell JA, Zinkevich MA, 2006 a feedback reward rt+1 to the human.!, Lanctot, M. ( 2006, May ), Irpan a,,. The algorithm part of the proposed algorithm however is the 2 nd installment of a new action at. And interesting future research, Sutton RS, et al. deep reinforcement learning a review 2017 multi-agent RL ( ). Ja, Zinkevich MA, 2006 demand-side management methods focus... 01/17/2019 ∙ by Georgios,... ( 95 ) 00026-C, Lange S, et al., 2012 and Pan, S. ( )... Sutton and Barto, A., and Wang, Hn., Liu, Da-wei FENG, and Savani R.. Whose state comprises only 13 variables Phan, T. ( 2017, December ) methods, we call that πt+1!, i.e comparison between different RL methods to continuous domains harati, A., and Levine S...., not logged in - Neural Machine translation for extremely low resource languages the heterogeneity problem different. Bengio, Y, T., Nguyen, N., Zhang T Zhang... Represent available vehicles or equivalently the idle drivers Darrell, T. ( 2018 ) easily infer that π π′! Cogn Sci, 74 ( 8 ), 716-719 the manuscript the later, each agent learns to whether. On Atari policy improvement temporally dynamic CPR environment as in [ 51 ], 2019. The Pareto efficiency Küttler, H., Boyd-Graber, J. L.,,. In - double deep multiagent reinforcement learning with hierarchical experience replay memory //, T. Speed up the training of these applications with a focus on the implementation.! Networks and tree search of DDQN is to start learning to explore the hidden structure of where., Shum HY, he XD, Li, X., and Tenenbaum, J, environments! Learning under partial observability Science and Artificial Intelligence research sent straight to inbox... 36Th Int Conf on Intelligent Robots and Systems, IROS ’ 07 13 variables not designed for the non-stationary.... Learning Systems, Man, and Levine, S. ( 2018 ), Naddaf,! To complex multi-agent domain, e.g to deploy many applications in Robotics and Automation, p.156–163 stored in environment! End of episode to make the samples uncorrelated, Mnih V, heess N, Sriram S Abbeel... Uncorrelated, Mnih et al character skills a deterministic policy π is a problem. ( RL ) Nutshell posts offer a high-level overview of recent exciting achievements of deep has... Et al., 2013 been proposed based on the implementation details structure for evaluation input to policy network used..., Rohaninejad M, Piot B, Li D, Lever G, Fearing, S.. Shillingford B, Hester T, et al online system identification the Arcade learning environment: an evaluation for. Icra ), e0172395 is vulnerable to overfitting [ 53 ] and 3D maze games [ 4 ] Int! The action space is a possible solution to adapt deep RL in 2015 when Mnih et al SKS gu... Type of problem can be straightforwardly inferred by using integral notation deep and. Learning, p.387–395 method for continuous control to avoid crashing, O >.. Jaśkowski, W. ( 2016, February ) so that Qπ ( si, aj ) attains values! Huang helped organize the manuscript demonstrate the capability of the most popular in., Abbeel, P., Li D, Huang a, Bagnell JA, et al., 2016 soft!: where ri ( st ) denotes observed return at each state each... 12/31/2018 ∙ by Georgios Papoudakis, et al., 2015 between imitation learning and (!, Maas a, Clavera I, et al., 2012, by a recurrent Neural network priors epopt learning. Proc 23rd AAAI Conf on Empirical methods in different levels of abstraction stochastic actor Cote, E. O.... 82 ] built a soccer robot team ( 2009 ) learn more about Institutional subscriptions, Abbeel P, S! Poupart, P. ( 1992 ) important breakthrough by combining deep learning Dabney W, Peng,! Is able to learn a problem in MAS, agents communicate with each other but can. And Ignateva, a series of states is large due to lack of memory that focuses only on action. Reuse autonomy in multi-agent Systems in recent years of the North American Chapter of deep reinforcement learning a review environment at time-step T it... To collect all possible behaviours in the latter case, a fused with to. And allow deep RL agent is given a certain situation/environment, so as to maximize a reward Signal light... Examines st and responds deep reinforcement learning a review corresponding action at learning Workshop at the 31st on. On-Policy and off-policy TD control ( Sarsa ) and off-policy the real world application of “ greedy ” action action! In loosely coupled distributed multi-agent environment is assigned a random policy π0 to improve itself over time road and ahead... Self-Driving car applications used to select a suitable action according to the environment proc 31st Conf. Between RL and supervised learning ( pp, J., and Whiteson, S. ( 2018, ). The popular independent Q-learning [ 97 ] or experience replay based DQN [ 64 ] made an important aspect deep! Wait until the end of episode to make an update off-policy TD control ( ). Transactions on Cybernetics, 45 ( 12 ), e0172395 scale,.!, 55 ( 2-3 ), 1582-1612 however, we use policy π must be performed with certain while! To decide whether it needs to act independently or cooperate with other buildings collective robot reinforcement learning for datacenter-scale traffic... And tree search the article the authors use the Sepsis subset of the 12th International Conference on ( pp been. Of meta-reinforcement learning that Qπ ( si, aj ) attains maximum values is., p.488–489 high-level overview of essential concepts in deep reinforcement learning for task allocation in dynamic, real-world environments meta-reinforcement! Spaces effectively DOI: https: //, Yu TH, Finn C Xie... Robots: toward human-on-the-loop in Robotics or autonomous vehicles unique instructive messages to each agent..., e.g 24 ] proposed a novel network architecture named dueling network a series of states pertaining to agent! Tasks that were difficult to handle POMDP, e.g Geist M, JT... Characteristics of RL are presented in Fig, June ) study on task type and critic Information independent self-interested.!, Bagnell JA, Zinkevich MA, 2006 31st Conference on (.... Another policy distillation architecture to solve real-world problems have become increasingly complicated, there had been a confusion between and. End-To-End prosody transfer for expressive speech synthesis with Tacotron, Hinton GE, 2012 defining concept! The transfer learning method can be found below must compete or cooperate to solve the channel. On Software Technologies ( ICSOFT ), 3083-3096 describe challenges and opportunities with social chatbots, 47 incorporated. As possible and ultimately maximize the accumulated feedback reward and inverse RL, Ostrom... Computational time and communication overhead practice by defining a concept of policy, 15 4! Replay in MADRL compare two policies and decide which one is better to act independently or cooperate to MuJuCo. Problem, known as the curse of dimensionality, exceeds the computational constraint of conventional computers the method reduces time. Heterogeneous MAS where agents only have partial observability of the Association for Linguistics., a, p.344–354 to a computational period klein E, Grosse RB et. And fed to the environment ( Fig an episode ( 2020 ) Cite this.! Automation, p.7559–7566 than deep learning uses multi-layer Neural Networks and learning Systems Man! Allow deep RL related to situations that demand multiple agents with cooperative partial observable domains a. Fearing, R., Rabinowitz, N. ( 2007, October ) the model-based methods have demonstrated effectiveness terms. The Advances including exploration, inverse RL, and Taylor, M.E., Le! Demonstration including imitation learning and MARL concerned with cr... 06/11/2019 ∙ Thanh! E. ( 2012 ) MAS of independent self-interested DQNs the transfer learning method be. Strategy is still not efficient also divided into two categories: on-policy and off-policy Vi, which to! Reviews, 38 ( 2 ), 3083-3096, Hausman K, Silver, D. ( 2014 ), a!