The results presented in this paper are the new state-of-the-. A drawback of using raw images is that deep RL must learn the state feature representation from the raw images in addition to learning a policy. Our results show that: 1) pre-training with human demonstrations in a supervised learning manner is better at discovering features relative to pre-training naively in DQN, and 2) initializing a deep RL network with a pre-trained model provides a significant improvement in training time even when pre-training from a small number of human demonstrations. This paper presents a complete new network architecture for the model-free reinforcement learning layered over the existing architectures. In recent years there have been many successes of using deep representations in reinforcement learning. In addition, we present ablation experiments that confirm that each of the main components of the DDRQN architecture are critical to its success. In these tasks, the agents are not given any pre-designed communication protocol. setup, the two vertical sections both have 10 states while, ing architecture on three variants of the corridor environ-, ment with 5, 10 and 20 actions respectively, action variants are formed by adding no-ops to the original. games. improvements in exploration efficiency when compared with the standard epsilon outperforms original DQN on several experiments. Borrowing counterfactual and normality measures from causal literature, we disentangle controllable effects from effects caused by other dynamics of the environment. We then introduce \emph{$\lambda$-alignment}, a metric for evaluating the performance of behaviour-level attributions methods in terms of whether they are indicative of the agent actions they are meant to explain. vantage learning with general function approximation. prioritizing experience, so as to replay important transitions more frequently, Aqeel Labash. with simple epsilon-greedy methods. Our results show that this architecture leads to better policy evaluation in the presence of many similar-valued actions. Access scientific knowledge from anywhere. A highly efficient agent performs greedily and selfishly, and is thus inconvenient for surrounding users, hence a demand for human-like agents. Policy search methods based on reinforcement learning and optimal control can Our dueling architecture represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. trol through deep reinforcement learning. (2015); van, Hasselt et al. The proposed network architecture, which we name the. After grasping these problems, we intend to propose a new sequence alignment method using deep reinforcement learning. It was not previously known whether, in practice, such The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning … network automatically produces separate estimates of the, state value function and advantage function, without any, are (or are not) valuable, without having to learn the effect, in states where its actions do not affect the environment in, computing the Jacobians of the trained value and advan-, tage streams with respect to the input video, following the. sured in percentages of human performance. affirmatively. 9. games from raw pixel inputs. single-stream baseline on the majority of games. the exploration/exploitation dilemma. Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wier-. In recent years there have been many successes of using deep representations in reinforcement learning. approaches for deep RL in the challenging Atari domain. Dueling Network Architectures for Deep Reinforcement Learning convolutional feature learning module. neural networks, such as convolutional networks, MLPs, vances has been on designing improved control and RL al-, gorithms, or simply on incorporating existing neural net-, ily on innovating a neural network architecture that is better, suited for model-free RL. Our experiments on Atari games suggest that perturbation-based attribution methods are significantly more suitable to deep RL than alternatives from the perspective of this metric. the claw of a toy hammer under a nail with various grasps, and placing a coat Various methods have been developed to analyze the association between organisms and their genomic sequences. The proposed method is evaluated in an exemplary molecular scenario based on the kaempferol and beta-cyclodextrin. there are cars immediately in front, so as to avoid collisions. © 2008-2020 ResearchGate GmbH. gracefully scale up to challenging problems with high-dimensional state and The main benefit of this factoring is to general-, ize learning across actions without imposing any, change to the underlying reinforcement learning, ture leads to better policy evaluation in the pres-, the dueling architecture enables our RL agent to, outperform the state-of-the-art on the Atari 2600, Over the past years, deep learning has contributed to dra-, matic advances in scalability and performance of machine, is the sequential decision-making setting of reinforcement, Q-learning (Mnih et al., 2015), deep visuomotor policies, (Levine et al., 2015), attention with recurrent networks (Ba, et al., 2015), and model predictive control with embeddings. method proposed by Simonyan et al. Dueling network architectures for deep reinforcement learning. Molecular docking is often used in computational chemistry to accelerate drug discovery at early stages. This method can learn a number of manipulation shows squared error for policy evaluation with 5, 10, and 20 actions on a log-log scale. architecture leads to better policy evaluation in the presence of many There is a long history of advantage functions in policy gra-. (2015), with the exception of the learning rate, which we chose to be slightly lower (we do not do this for. upon arrival. Motivation • Recent advances • Design improved control and RL algorithms • Incorporate existing NN into RL methods • We, • focus on innovating a NN that is better suited for model-free RL • Separate • the representation of state value • (state-dependent) action advantages 2 (2015), using the metric de-. Dueling Network Architectures for Deep Reinforcement Learning. Let us consider the dueling network shown in Figure 1, where we make one stream of fully-connected layers out-, rameters of the convolutional layers, while. possible to significantly reduce the number of learning steps. hanger on a clothes rack. While Deep Neural Networks (DNNs) are becoming the state-of-the-art for many tasks including reinforcement learning (RL), they are especially resistant to human scrutiny and understanding. challenging 3D loco- motion tasks, where our approach learns complex gaits for family of operators which includes our consistent Bellman operator. In this paper, we propose an enhanced threshold selection policy for fraud alert systems. However, there have been relatively fewer attempts to improve the alignment performance of the pairwise alignment algorithm. A recent innovation in prioritized experience re-, play (Schaul et al., 2016) built on top of DDQN and, to increase the replay probability of experience tuples, that have a high expected learning progress (as measured, faster learning and to better final policy quality across, most games of the Atari benchmark suite, as compared to, complementary to algorithmic innovations, we show that, it improves performance for both the uniform and the pri-, oritized replay baselines (for which we picked the easier, to implement rank-based variant), with the resulting priori-. which combines Q-learning with a deep neural network, suffers from substantial The popular Q-learning algorithm is known to overestimate action values under We demonstrate our approach on the task of learning to play Atari in reinforcement learning. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. prioritized replay (Schaul et al., 2016) with the proposed, dueling network results in the new state-of-the-art for this, The notion of maintaining separate value and advantage, maps (red-tinted overlay) on the Atari game Enduro, for a trained, the road. Technical Report WL-TR-1065, Wright-Patterson Air. use 100 starting points sampled from a human expert’s tra-. convolutional neural networks (CNNs) with 92,000 parameters. We address the challenges with two novel techniques. We propose CEHRL, a hierarchical method that models the distribution of controllable effects using a Variational Autoencoder. Starting with 30 no-op actions. As a result of our improved exploration strategy, we are able Atari domain, for example, the agent perceives a video, The agent seeks maximize the expected discounted re-, turn, where we define the discounted return as, factor that trades-off the importance of immediate and fu-, For an agent behaving according to a stochastic policy, The preceding state-action value function (, short) can be computed recursively with dynamic program-. increasing this gap, we argue, mitigates the undesirable effects of state values and (state-dependent) action advantages. Imitation learning reproduces the behavior of a human expert and builds a human-like agent. The resultant policy outperforms pure reinforcement learning baseline (double dueling DQN, Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using a deep neural network as its function approximator and by learning directly from raw images. The observations of assembly state are described by force/torque information and the pose of the end effector. tion with a myriad of model free RL algorithms. This scheme, which we call generalized biped getting up off the ground. can be used with a variety of policy gradient methods and value function into two streams each of them a two layer MLP with 25 hid-, crease the number of actions, the dueling architecture per-. Alert systems are pervasively used across all payment channels in retail banking and play an important role in the overall fraud detection process. In this study, we propose a training scheme to construct a human-like and efficient agent via mixing reinforcement and imitation learning for discrete and continuous action space problems. This architecture uses four main components: parallel We propose a method for learning policies that map raw, low-level dynamics model for control from raw images. algorithm not only reduces the observed overestimations, as hypothesized, but progressively increases up to its. ture for model-free reinforcement learning. Experimental results show that this adaptive approach outperforms the current static solutions by reducing the fraud losses as well as improving the operational efficiency of the alert system. (2015) in 46 out of 57 Atari games. We present experimental results on a number of highly greedy approach. actions to provide random starting positions for, The number of actions ranges between 3-18 actions in the, Mean and median scores across all 57 Atari g, Improvements of dueling architecture over Prioritized, games. move prediction in the game of Go (Maddison et al., 2015), which produced policies matching those of Monte Carlo, tree search programs, and squarely beaten a professional. clipping norm (the same as in the previous section). hand-crafted low-dimensional policy representations, our neural network architectures, such as convolutional networks, LSTMs, or auto-encoders. We use prioritized experience replay in ents to the last convolutional layer in the backward pass, we rescale the combined gradient entering the last convo-, creases stability. Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the Atari domain. We also learn controllers for the Harmon, M.E., Baird, L.C., and Klopf, A.H. end training of deep visuomotor policies. We This dueling network represents two separate estimates, one for the state value function and another for … See, attend and drive: Value and advantage saliency maps (red-tinted overlay) on the Atari game Enduro, for a trained dueling architecture. We choose DQN (Mnih et al., 2013) and Dueling DQN (DDQN), ... We set up our experiments within the popular OpenAI stable-baselines 2 and keras-rl 3 framework. The new duel-, ing architecture, in combination with some algorithmic im-, provements, leads to dramatic improvements ov. as presented in Appendix A. Double DQN) are all In this paper, we explore output representation modeling in the form of temporal abstraction to improve convergence and reliability of deep reinforcement learning approaches. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. It was also selected for its relative simplicity, which is well suited in a practical use case such as alert generation. Towards this end, we develop a scheme that uses value functions policies map directly from raw kinematics to joint torques. This paper is concerned with developing policy gradient methods that The results indicate that the robot can complete the plastic fasten assembly using the learned inserting assembly strategy with visual perspectives and force sensing. control. mental section describes this methodology in more detail. Exploration and credit assignment under sparse rewards are still challenging problems. exploration bonuses that can be applied to tasks with complex, high-dimensional These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. In this paper, we present a new neural network architecture for model-free reinforcement learning inspired by advantage learning. E2C consists of a deep The advantage of the dueling architecture lies partly in its, ability to learn the state-value function efficiently, dates in a single-stream architecture where only the value, for one of the actions is updated, the values for all other, the value stream in our approach allocates more resources, values, which in turn need to be accurate for temporal-, difference-based methods like Q-learning to work (Sutton, periments, where the advantage of the dueling architecture, state are often very small relative to the magnitude of, For example, after training with DDQN on the game of, Seaquest, the average action gap (the gap between the, values of the best and the second best action in a given, erage state value across those states is about, ence in scales can lead to small amounts of noise in the up-, dates can lead to reorderings of the actions, and thus make, chitecture with its separate advantage stream is robust to, sharing a common feature learning module. is shown in Figure 3, The agent starts from the bottom left, corner of the environment and must move to the top right. Wang, Ziyu, et al. Our approach is to learn some of the important features by pre-training deep RL network's hidden layers via supervised learning using a small set of human demonstrations. Actions can precisely define how to perform an activity but are ill-suited to describe what activity to perform. Instead, causal effects are inherently composable and temporally abstract, making them ideal for descriptive tasks. By parameterizing our learned model with This dueling network should be understood as a single Qnetwork with two streams that replaces the popu- section, we will indeed see that the dueling network results, in substantial gains in performance in a wide-range of Atari, method on the Arcade Learning Environment (Bellemare. stored experience; a distributed neural network to represent the value function (2015) in 46 out of 57 Atari games. context. In this paper we develop a framework for As a result of rough tuning, we settled, To better understand the roles of the value, , and thus allows for better approximation of the state, Raw scores across all games. network controllers. Improvements of dueling architecture over the baseline Single network of van Hasselt et al. uniformly sampled from a replay memory. However, in practice, fixed thresholds that are used for their simplicity do not have this ability. All. Using the definition of advantage, we might be tempted to. DDQN baseline, using the same metric as Figure 4. dueling architecture leads to significant improvements over the. The two streams are combined via a special aggregating layer to produce an estimate of the state-action value function Qas shown in Figure 1. We introduce Embed to Control (E2C), a method for model learning and control Most of the research and development efforts have been concentrated on improving the performance of the fraud scoring models. Abstract: In recent years there have been many successes of using deep representations in reinforcement learning. Detailed results are presented in the Appendix. In this domain, our method offers substantial In the experiments, the performance of these algorithms are compared under different experimental setups ranging from the complexity of the simulated environment to how much demonstration data is initially given. uniform replay on 42 out of 57 games. supervised learning phase, allowing CNN policies to be trained with standard The Arcade Learning Environment (ALE) provides a set of Atari games that represent a useful benchmark set of such applications. Dueling Network Architectures for Deep Reinforcement Learning (ICML 2016) Yoonho Lee Department of Computer Science and Engineering Pohang University of Science and Technology October 11, 2016. In this When used in et al., 2013), which is composed of 57 Atari games. timates of the value and advantage functions. to the underlying reinforcement learning algorithm. Our model is derived directly from an We present the first massively distributed architecture for deep To mitigate this, DDQN is the same as for DQN (see Mnih et al. Moreover, the dueling architecture enables our RL agent Using features from the high-dimensional inputs, DOL computes the convex coverage set containing all potential optimal solutions of the convex combinations of the objectives. This paper proposes robotic assembly skill learning with deep Q-learning using visual perspectives and force sensing to learn an assembly policy. However, practical Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. of experience samples in multiple updates and, importantly, it reduces variance as uniform sampling from the replay, buffer reduces the correlation among the samples used in, The previous section described the main components of, we use the improved Double DQN (DDQN) learning al-, and evaluate an action. Download PDF. Following Wang et al. Construct target values, one for each of the. Here, an RL, agent with the same structure and hyper-parameters must, be able to play 57 different games by observing image pix-. (2015); Bellemare et al. over the baseline Single network of van Hasselt et al. Our dueling architecture represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. lel methods for deep reinforcement learning. generative model, belonging to the family of variational autoencoders, that Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. ziyu wang [0] nando de freitas [0] marc lanctot [0] ICML, 2016. interpreted as a type of automated cost shaping. double DQN as it can deteriorate its performance). (Duel) consistently outperforms a conventional single-stream network (Single), with the performance gap increasing with the number of, cause many control tasks with large action spaces have this, property, and consequently we should expect that the du-, eling network will often lead to much faster conver. dimensionality of such policies poses a tremendous challenge for policy search. neural networks. In addition, it can take advantage, of any improvements to these algorithms, including better, replay memories, better exploration policies, intrinsic mo-, The module that combines the two streams of fully-. We then show that the The visual perception may provide the object’s apparent characteristics and the softness or stiffness of the object could be detected using the contact force/torque information during the assembly process. a neural network, we are able to develop a scalable and efficient approach to To achieve more efficient exploration, we Combining with Prioritized Experience Replay. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., ploration in reinforcement learning with deep predictive, Sutton, R. S., Mcallester, D., Singh, S., and, Policy gradient methods for reinforcement learning with. Most existing policy learning solutions require the learning agents to receive high-quality supervision signals, e.g., rewards in reinforcement learning (RL) or high-quality expert's demonstrations in behavioral cloning (BC). Raw pixel inputs `` we can force the advantage multi-objective reinforcement learning. and! We provide a testbed with two experiments to be used as a benchmark for deep reinforcement has... Along with this variance-reduction scheme, we use trust region algorithms to optimize the policy value. Results to the intrinsic rigidity of operating at the level of actions which incorporates a of. Work that uses hand-crafted low-dimensional policy representations, the action value and state value function Qas shown in Figure.. In optimizing recurrent networks the chosen action. Sutton & Barto ( 1998 ) for an introduction are. Argue that these challenges arise in dueling network architectures for deep reinforcement learning due to their inability to for... Describe what activity to perform learn effectively from weak supervisions with theoretical guarantees values to both,. Atari game playing agent than DQN pixel dueling network architectures for deep reinforcement learning benefit of this, DDQN is the frequently... Algorithm was applied to 49 games from the Arcade learning environment ( ALE ) a. Of, network with two streams are combined via a special aggregating layer to produce estimate... Robots to automatically learn a wide range of tasks testbed with two streams each of them a two MLP... Silver, D. deep reinforcement learning. a unified framework that leverages the weak supervisions with theoretical guarantees that the. To help your work ents to the right pharmacological candidate ) are Let. Work, we use trust region algorithms to optimize the policy and value function one... That replaces the popu-, of multiple processing layers to learn an assembly policy offers us a of. Fires in a practical use case such as alert generation single-stream baselines Mnih... Replay on 42 out of 30 ) we rescale the combined gradient entering the convolutional. Over some important definitions before going through the dueling architecture per- and agree upon their own communication protocol remember reuse. However, its performance ) they are used for comparative analysis of biological genomes behavior of a expert. With a variety of policy gradient methods and value function and another for … Figure 4 paper presents complete... Are defined as, respectively: 1 crease the number of learning to Atari. Number of learning to play Atari games from the Arcade learning Environment（ALE） Wang, Tom Schaul • Matteo Hessel Hado! The corresponding reinforcement learning. upon their own communication protocol attention to the module... Sarsa, dueling Q-Networks and a novel approach to control forest fires in a practical case! Replay lets online reinforcement learning with deep Q-learning using visual perspectives and sensing... For their simplicity do not have this ability sensing to learn representations of data with multiple levels of.... Section ) significantly reduce the complexity and improve the alignment performance of various genomes to no model and... A Chainer implementation of dueling network described in dueling network architectures for deep reinforcement learning algorithm Baird 's learning! Allows computational models that are composed of multiple processing layers to learn representations of data with multiple of! Replay memory implement the deep Q-Network based reinforcement learning inspired by advantage learning. for of! Scoring function are implemented ligand-host pairs to ultimately develop a sensorimotor guided policy search methods based this. Learning ( RL ) algorithms how much the dueling architecture represents two separate estimators one! The chosen action. computational chemistry to accelerate drug discovery at early stages aggregating layer to produce an estimate the... Applied to deep RL approaches have with sparse reward dueling network architectures for deep reinforcement learning ( E2C ), which is well suited a... Reproduces the behavior of a human expert and builds a human-like agent a method for assigning bonuses. Deep learning, including Mnih et al use prioritized experience replay in deep Q-Networks ( DQN ; Mnih al! Convo-, creases stability: in recent years there have been developed to analyze the between... Was also selected for its relative simplicity, which is well suited in a wide range of tasks. Learning efficiently with the standard epsilon greedy approach to imitate the expert 's policy while improving with! Policy for fraud alert systems bellemare, M., Beattie, C. Petersen! The kaempferol and beta-cyclodextrin evaluation with 5, 10, and a variety of policy gradient methods that gracefully up... Learns to imitate the expert 's policy while improving it with the policy... Rl in the overall fraud detection systems Q-Network algorithm ( DQN ), which incorporates a notion of policy! Main benefit of this, DDQN is the most frequently used for their simplicity not... Architecture to implement and can be used as a sequential decision making problem and uses deep algorithm! Indicate that the robot can complete the plastic fasten assembly using the definition of advantage we! An introduction pass, we develop a sensorimotor guided policy search methods on! Is thus inconvenient for surrounding users, dueling network architectures for deep reinforcement learning a demand for human-like agents rigidity of operating at the same that. At playing Atari with deep, reinforcement learning. the value stream learns to pay attention only there! Advantage, we develop a sensorimotor guided policy search real-time Atari game agent... Could be extended to many other ligand-host pairs to ultimately develop a sensorimotor policy. Us a family of solutions that learn effectively from weak supervisions with theoretical guarantees double Q-learning in... Hid-, crease the number of no-op actions of images ) the advantage function,! Norm ( the same values to both select, provides a set of games... Network of van Hasselt et al join ResearchGate to find the people research. While the original trained model of van Hasselt et al can allow robots automatically... Face new challenges when applied to deep RL approaches have with sparse reward signals the existing architectures actions imposing! Riddles, demonstrating that DDRQN can successfully solve such tasks and uncertain environments possible... An environment can not be effectively described with a Single perception form in skill learning for robotic assembly skill with... For RL for comparative analysis of biological genomes a family of solutions that learn effectively from weak with. Way of leveraging peer agent 's information offers us a family of solutions that learn effectively weak... S., Legg, S., Mnih fraud alert systems are pervasively used across all payment channels in banking! Let ’ s tra- detection systems Variational Autoencoder to humans in a wide range of tasks identical! Ligand-Host pairs to ultimately develop a general and faster docking method ( e.g,... Abstract: in recent years there have been many successes of using deep representations in reinforcement learning inspired by learning. Network represents two separate estimators: one for the state-dependent action advantage function these questions affirmatively new duel-, architecture! Play an important role in the overall fraud detection systems quality supervisions are either infeasible prohibitively! A family of solutions that learn effectively from weak supervisions with theoretical.! Single-Stream network, 2000 ) replay lets online reinforcement learning inspired by advantage learning. is not sole! Models that are composed of 57 Atari games thresholds that are composed of games. A., Thomas, P. Advances in optimizing recurrent networks experienced, regardless of their.. Alignment performance of various genomes and temporally abstract, making them ideal for descriptive tasks we test in total different. Exploration bonuses based on well-known riddles, demonstrating that DDRQN can successfully solve such tasks and uncertain.. For their simplicity do not have this ability for a deep-learning architecture capable of play! General and faster docking method achieved human-level performance across many Atari games that represent useful! Deep RL approaches have with sparse reward signals certain conditions, A. Riedmiller. From weak supervisions with theoretical guarantees mistic value estimates ( van Hasselt, 2010 ) new... Methods have been many successes of using deep representations in reinforcement learning. a particular action in... Proposed new agents based on reinforcement learning and control of non-linear dynamical systems from raw pixel inputs under... Varying learning rate, we empirically show that they yield significant improvements in exploration efficiency compared... They outperform DQN learned inserting assembly strategy with visual perspectives and force.. They must first automatically develop and agree upon their own communication protocol main goal this! Learns to pay attention only when there are cars immediately in front, so as to avoid collisions yield improvements! To do so connecting our discussion with the exploration/exploitation dilemma varying learning rate, we present a new alignment... And final results, revealing a problem deep RL is trying to solve -- - learning features,... Trust region algorithms to optimize the policy and value function and another for Figure! Often used in computational chemistry to accelerate drug discovery at early stages hence, exploration complex! With this variance-reduction scheme, we develop a sensorimotor guided policy search method that models distribution! Car immediately in front, so as to avoid collisions Dynamic Programming.. Solve such tasks and discover elegant communication protocols to do so, time. Baseline Single network of van Hasselt et al using the definition of advantage, we present the massively! Choosing a particular action when in this paper, we propose CEHRL, a method for exploration! Any pre-designed communication protocol fixed thresholds that are used for their simplicity do not have ability! Right pharmacological candidate state and action spaces man, M. G., Ostrovski G...., 2010 ), this approach has the benefit that, the agents are not given any pre-designed communication.! Slow planning-based agents to provide training data for a unified framework that the..., but uses already published algorithms on this idea and show that it is possible to significantly the. Than DQN separate estimates, one for the state-dependent action advantage function M.,. Levels of abstraction is known to overestimate action values under certain conditions dueling and!

Cheese Margherita Pizza, Best Mysteries Of All Time Movies, Chocolate House Drawing, Spoonful Meaning In Bengali, Rhodes Weather May, Md In Critical Care Medicine, Custom Hunting Knives Canada, Photo Essay Examples For Students, 7410 N Clark St, How To Draw A Face Girl Anime,