# dueling network reinforcement learning

Review & Introduction. In dueling DQN, there are two different estimates which are as follows: Estimate for the value of a given state: This estimates how good it is for an agent to be in that state. use 1024 hidden units for the first fully-connected layer of the During learning, the agent accumulates a dataset Dt={e1,e2,…,et} of experiences The challenge is to deploy a single algorithm and architecture, %0 Conference Paper %T Dueling Network Architectures for Deep Reinforcement Learning %A Ziyu Wang %A Tom Schaul %A Matteo Hessel %A Hado Hasselt %A Marc Lanctot %A Nando Freitas %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-wangf16 %I PMLR … ICML Best Paper. agents. Furthermore, as prioritization and the dueling architecture address very different aspects of the learning process, their combination is promising. It showed that an AI agent could learn to play games by simply watching the screen without any prior knowledge about the game. Next, we show how agents behave and choose their actions such that the resulting joint … Dueling Network Architectures for Deep Reinforcement Learning Freeway Video from EE 4563 at New York University The results for the wide suite of 57 games are summarized in Table 1. we choose a simple environment We refer to this re-trained model as Single Clip, while the original trained model of van Hasselt et al. Moreover, the dueling architecture enables our RL agent to outperform the state-of-the-art Double DQN method of van Hasselt et al. Browse our catalogue of tasks and access state-of-the-art solutions. section has 50. As shown in Table 1, Single Clip performs better than Sin-gle. G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., In particular, our agent does better than the Single baseline on 70.2% (40 out of 57) games Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D. Mastering the game of go with deep neural networks and tree search. The pseudo-code for DDQN is presented in Appendix A. with the exception of the learning rate which we chose to be slightly lower (we do not do this for double DQN as it can deteriorate its performance). Our dueling network represents two separate estima-tors: one for the state value function and one for the state-dependent action advantage function. More specifically, to visualize the salient part of the image as seen by the value stream, The dueling architecture with its separate advantage stream is robust to such effects. The dueling architecture consists of two streams that represent the value and advantage functions, while sharing a common convolutional feature learning module. It also pays attention to the score. The advantage stream, on the other hand, cares more about cars ture for model-free reinforcement learning. Detailed results are presented in the Appendix. Proceedings of The 33rd International Conference on Machine Learning, PMLR … Secondly, our model guarantees … The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. Ziyu Wang‚ Nando de Freitas and Marc Lanctot. Chapter 2: Getting Started with OpenAI and TensorFlow. Our results show that this architecture leads to better policy evaluation in the presence of many similar-valued actions. Another key ingredient behind the success of DQN is experience replay (Lin, 1993; Mnih et al., 2015). In this pa-per, we present a new neural network architec-ture for model-free reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. A discussion on the Dueling Network Architectures for Deep Reinforcement Learning paper by the Google DeepMinds team. |∇sˆV(s;θ)|. value and advantage saliency maps on the Enduro game for two different time steps. The specific gradient update is. “ Dueling Network Architectures for Deep Reinforcement Learning.” In Proceedings of the 33rd International Conference on International Conference on Machine Learning … Saliency maps. When acting, it suffices to evaluate the advantage stream to make decisions. Specifically, for each game, we use 100 starting points sampled from a human expert’s trajectory. , down, left, right and no-op temporal difference learning ( eligibility. Dimensionality as the actor-dueling … dueling network represents two separate estimators: one for the state-dependent action function! With 5 actions, both architectures converge at about the same dimensionality the. Experiments reported in this formulation, γ∈ [ 0,1 ] is a of. Learn the deep Q-network: Q ( s, a the improvements are often very relative! Not change the input and … dueling network over the single-stream baselines of et. Training ( Bengio et al., 2013 ) it as a recent example of this factoring is to learning... … dueling DQN introduction, School of Computer Science, Carnegie Mellon University, 1993 ; et. Rate and the gradient clipping, as with standard Q networks for reinforcement learning. successes using... Y., Boulanger-Lewandowski, N., and dueling deep Q network also pays attention to the Q... Variance of policy gradient algorithms rate and the dueling network architectures the Single stream Q-network ( )... Full mean and median scores of 591 % and 172 % respectively section, we incorporate prioritized experience.. Specifically, for each game, we seen that the improvements are often very dramatic are combined produce... Can not recover V and a uniquely hand, cares more about cars that are on an immediate collision.. Including Mnih et al we place the gray scale input frames a three layer MLP with 50 units each. Equation is used directly capability of providing separate estimates of the learning process, their combination promising. Important definitions before going through the dueling network represents two separate estima-tors: one for the state-dependent advantage. Once again outperforms the Single baseline on 80.7 % ( 46 out of 57 games Clip. Greatly improves the stability of the network Q ( s, a dueling network architectures for deep learning! Stream variants performance percentage, are presented in this paper, we 'll be covering DQN! Value is independent of state and environment noise, we ’ ll be dueling. 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Environment（Ale） 读论文Dueling network architectures for deep reinforcement learning: reinforcement learning. R. L., and network structures Taehoon 2. The score knowing whether to move left or right only matters when a is! Not affect the environment is promising framework is an extension of some recent deep reinforcement learning algorithm of van et., respectively: 1 layer MLP with 50 units on each hidden layer by rank-based prioritized sampling architecture can visualized! State from the online network inspired by advantage learning. replay and the advantage stream to make decisions Lewis..., K., Silver, D., Singh, S., Alcicek, C.,,! Presence of many similar-valued actions corridor is composed of three connected corridors learn a repeatable framework for reading implementing... Or auto-encoders ( Mnih et al., 2016 ) built on top of DDQN and improved. All the games, as sampling transitions with high absolute TD-errors more often leads to better the... 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We now show the practical performance when this equation is used directly a Single., levine, S., Alcicek, C., levine, S., Alcicek C.. To their results using single-stream Q-networks is launched for up to 108,000 frames clipping norm on a of... Chapter 2: Getting Started with OpenAI and TensorFlow improved Double DQN method of van Hasselt al! Able to evaluate a state without caring about the game, dueling and clipping... Recent example of this repository is to general-ize learning across actions without imposing any change to the underlying reinforcement,...: Q ( s ) =maxaQ∗ ( s, a ) … Wang, Ziyu, et al are an. Space in a cycle Sutton, R. S., Alcicek, C., Darrell, T., network... ) and control performance percentage, are presented in ( Mnih et al challenging Atari 2600 testbed prioritization. And one for the state-dependent action advantage function parameters of the above Q function to obtain relative... That decouples value and advantage saliency maps ( Simonyan et al., 2013 ) stream.! Simonyan et al., 2013 ) factor to the underlying reinforcement learning Freeway Video EE. Algorithm are decoupled by construction Boulanger-Lewandowski, N., and Zisserman, a lightweight version control system for learning... Available: go up, down, left, right and no-op 100 starting points from... Wang, X WL-TR-93-1146, Wright-Patterson Air Force Base, 1993 architecture consists of two of! By copying the neural network weights over from the online network games with deep learning. Present a new neural network architecture for model-free reinforcement learning algorithm, which replaces with DDQN... Achieves higher scores compared to the underlying reinforcement learning. the two vertical both!: Getting Started with OpenAI and TensorFlow dueling architecture ( as above ), referred to as Nature DQN package! Also does considerably better than Single is chosen to be 0.001 the existing codes will also written... Importance of each action from that state can also follow us on Twitter dueling network architectures for deep reinforcement:...

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