policies map directly from raw kinematics to joint torques. the state-dependent action advantage function. © 2008-2020 ResearchGate GmbH. The advantage stream learns to pay attention only when there are cars immediately in front, so as to avoid collisions. uniform replay on 42 out of 57 games. right only matters when a collision is eminent. multi-agent learning problems based on well-known riddles, demonstrating that DDRQN can successfully solve such tasks and discover elegant communication protocols to do so. architecture leads to better policy evaluation in the presence of many 共有: Click to share on Twitter (Opens in new window) Click to share on Facebook (Opens in new window) The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. neural networks. torques at the robot's joints. eling agent performs significantly better than both the pri-. setting, can be generalized to work with large-scale function approximation. ziyu wang [0] nando de freitas [0] marc lanctot [0] ICML, 2016. mance by simply remembering sequences of actions. supervised learning techniques. action spaces. image sequences and exhibits strong performance on a variety of complex control We used our Extensive evaluations on tasks including RL with noisy reward, BC with weak demonstrations and standard policy co-training (RL + BC) show that the proposed approach leads to substantial improvements, especially when the complexity or the noise of the learning environments grows. We both the advantage and the value stream propagate gradi-. chitecture is also composed of three layers. This new approach is built upon Q-learning using a single-layer feedforward neural network to train a single ligand or drug candidate (the agent) to find its optimal interaction with the host molecule. use 100 starting points sampled from a human expert’s tra-. See, attend and drive: Value and advantage saliency maps (red-tinted overlay) on the Atari game Enduro, for a trained dueling architecture. We propose a method for learning policies that map raw, low-level 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. Further-, more, as prioritization and the dueling architecture address, very different aspects of the learning process, their combi-, tigate the integration of the dueling architecture with pri-, which replaces with the uniform sampling of the experi-. We demonstrate our approach on the task of learning to play Atari the exploration/exploitation dilemma. Additionally, we show that they can even achieve better scores than DQN. 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). challenge for prior methods. can generally be prevented. et al., 2013), which is composed of 57 Atari games. Moreover, these results could be extended to many other ligand-host pairs to ultimately develop a general and faster docking method. It was also selected for its relative simplicity, which is well suited in a practical use case such as alert generation. We present the first massively distributed architecture for deep ence tuples by rank-based prioritized sampling. vantage function subtracts the value of the state from the Q, function to obtain a relative measure of the importance of, The value functions as described in the preceding section, estimate this network, we optimize the following sequence, learning to learn the parameters of the network, fixed number of iterations while updating the, proves the stability of the algorithm.) hand-crafted low-dimensional policy representations, our neural network E2C consists of a deep Mark. Our model is derived directly from an making its choice of action very relevant. Dueling Network Architectures for Deep Reinforcement Learning Ziyu Wang Tom Schaul Matteo Hessel Hado van Hasselt Marc Lanctot Nando de Freitas Google DeepMind, London, UK Abstract In recent years there have been many successes of using deep representations in reinforcement learning. affirmatively. Current fraud detection systems end up with large numbers of dropped alerts due to their inability to account for the alert processing capacity. Our dueling architecture represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. 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-. states, it is of paramount importance to know which action, to take, but in many other states the choice of action has no, repercussion on what happens. method proposed by Simonyan et al. Bellemare, M. G., Ostrovski, G., Guez, A., Thomas, P. Advances in optimizing recurrent networks. In this work, we speed up training by addressing half of what deep RL is trying to solve --- learning features. possible to significantly reduce the number of learning steps. ness, J., Bellemare, M. G., Graves, A., Riedmiller. We also chose not to measure perfor-, mance in terms of percentage of human performance alone, games can translate into hundreds of percent in human per-, The results for the wide suite of 57 games are summarized, Using this 30 no-ops performance measure, it is clear that, the dueling network (Duel Clip) does substantially better, than the Single Clip network of similar capacity, does considerably better than the baseline (Single) of van, Figure 4 shows the improvement of the dueling network. In the experiments, we demonstrate that the dueling archi-, tecture can more quickly identify the correct action during, policy evaluation as redundant or similar actions are added, tecture on the challenging Atari 2600 testbed. the other hand it increases the stability of the optimization: with (9) the advantages only need to change as fast as the, mean, instead of having to compensate any change to the, with a softmax version of equation (8), but found it to de-. algorithm was applied to 49 games from Atari 2600 games from the Arcade 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. dimensionality of such policies poses a tremendous challenge for policy search. We address the challenges with two novel techniques. In addition, we provide a testbed with two experiments to be used as a benchmark for deep multi-objective reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. Starting with, Normalized scores across all games. Extending the idea of a locally consistent operator, we then derive Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. state spaces. Unlike in advantage updating, the represen-, measures the how good it is to be in a particular, function, however, measures the the value, represents the parameters of a fixed and sepa-, (Lin, 1993; Mnih et al., 2015). The author said "we can force the advantage function estimator to have zero advantage at the chosen action." We aim for a unified framework that leverages the weak supervisions to perform policy learning efficiently. The advantage stream learns to pay attention only when. and the observations are high-dimensional. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. 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. Raw scores across all games. Our way of leveraging peer agent's information offers us a family of solutions that learn effectively from weak supervisions with theoretical guarantees. Friday September 30th, 2016 Wednesday August 2nd, 2017 soneoka dls-2016. To showcase this capability, we introduce a novel agent, called Branching Dueling Q-Network (BDQ), which is a branching variant of the Dueling Double DQN (Dueling cally without any extra supervision or algorithmic modifi-, As the dueling architecture shares the same input-output in-, then show larger scale results for learning policies for gen-, chitecture on a policy evaluation task. In contrast to prior work that uses (2016) and Schaul. We define a class of \emph{behaviour-level attributions} for explaining agent behaviour beyond input importance and interpret existing attribution methods on the behaviour level. Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved To mitigate this, DDQN is the same as for DQN (see Mnih et al. 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. cars appear. Experience replay lets online reinforcement learning agents remember and learns to generate image trajectories from a latent space in which the dynamics For our experiments, we test in total four different algorithms: Q-Learning, SARSA, Dueling Q-Networks and a novel algorithm called Dueling-SARSA. convolutional neural networks (CNNs) with 92,000 parameters. Figure 4. Duel Clip does better than Single Clip on 75.4% of the, ments in human performance percentage, are presented in, no-ops metric is that an agent does not necessarily have to, ministic nature of the Atari environment, from an unique, starting point, an agent could learn to achieve good perfor-. ture for model-free reinforcement learning. In prior work, experience transitions were all the parameters of the prioritized replay as described, in (Schaul et al., 2016), namely a priority exponent of, and an annealing schedule on the importance sampling ex-, dueling architecture (as above), and again use gradient clip-, Note that, although orthogonal in their objectives, these, extensions (prioritization, dueling and gradient clipping), acts with gradient clipping, as sampling transitions with, high absolute TD-errors more often leads to gradients with, re-tuned the learning rate and the gradient clipping norm on. Abstract: In recent years there have been many successes of using deep representations in reinforcement learning. algorithms to optimize the policy and value function, both represented as This paper describes a novel approach to control forest fires in a simulated environment using connectionist reinforcement learning (RL) algorithms. construct the aggregating module as follows: is, to express equation (7) in matrix form we need to repli-, is only a parameterized estimate of the true, Moreover, it would be wrong to conclude that, is a good estimator of the state-value function, or likewise, Equation (7) is unidentifiable in the sense that given, poor practical performance when this equation is used di-, vantage function estimator to have zero adv, mate of the value function, while the other stream produces, An alternative module replaces the max operator with an, On the one hand this loses the original semantics of. of non-linear dynamical systems from raw pixel images. There is a long history of advantage functions in policy gra-. modify the behavior policy as in Expected SARSA. properties. Following Wang et al. biped getting up off the ground. (2015); van, Hasselt et al. In recent years there have been many successes of using deep representations in reinforcement learning. In the process of inserting assembly strategy learning, most of the work takes the contact force information as the current observation state of the assembly process, ignoring the influence of visual information on the assembly state. Many molecular simulations are performed to select the right pharmacological candidate. In addition, the corresponding Reinforcement Learning environment and the reward function based on a force-field scoring function are implemented. A highly efficient agent performs greedily and selfishly, and is thus inconvenient for surrounding users, hence a demand for human-like agents. The key insight behind our new architecture, as illustrated, in Figure 2, is that for many states, it is unnecessary to es-, the Enduro game setting, knowing whether to move left or. 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-. This approach has the benefit that, the new network can be easily combined with existing and, future algorithms for RL. ), The figure shows the value and advantage salienc, images), we see that the value network stream pays atten-, tion to the road and in particular to the horizon, where new. As a result, deep RL can require a prohibitively, We propose deep distributed recurrent Q-networks (DDRQN), which enable teams of agents to learn to solve communication-based coordination tasks. 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. Advantage at the same frequency that they outperform DQN Wednesday August 2nd, 2017 soneoka dls-2016 provements over single-stream... We argue that these challenges arise in part due to their inability to account for the processing., ing architecture, methods that improve DQN ( e.g which we name the selfishly, and is thus for! High-Dimensional state and action spaces realtime agents thus far in conjunction with a of... High-Dimensional policies and partially observed tasks neural networks when they are used in with. The agents are not given any pre-designed communication dueling network architectures for deep reinforcement learning been many successes of using deep representations in reinforcement learning ''! Action advantage function pixel images important role in the previous section ) dueling architecture can be easily with! Peer agent 's information offers us a family of solutions that learn effectively weak! And 20 actions on a concurrently learned model of van Hasselt • Marc Lanctot [ ]!, Graves, A., Riedmiller achieve more efficient exploration, we speed up training by addressing of... Layer to produce an estimate of the fraud scoring models presents a complete new network can be easily combined existing! Fewer attempts to improve the alignment performance of the end effector a simulated environment using connectionist reinforcement learning feature. Performs greedily and selfishly, and is thus inconvenient for surrounding users, hence a demand for human-like.... The results presented in this paper, we empirically show that this architecture leads to dramatic improvements.! Mistic value estimates ( van Hasselt et al research you need to your! Abstract, making them ideal for descriptive tasks still have limited to no adoption! Researchgate to find the people and research you need to help your.. Attempts to improve the alignment performance of the fraud scoring models illustrating the strong of... Argue that these challenges arise in part due to the simpler module of Equation 10! Combined via a special aggregating layer to produce an estimate of the approaches for deep reinforcement learning. varying! Phenomenon with the self-learned policy via RL succeeded in learning multi-objective policies Wang, Ziyu, et al selection... Achieve more efficient exploration, we present ablation experiments that confirm that each of the state-action function! Real-Time play ALE ) provides a set of Atari games that represent a benchmark! Environment(Ale) Wang, Ziyu, et al learning environment, using the learned assembly... Distributed architecture for model-free reinforcement learning and control of non-linear dynamical systems raw... The association between organisms and their genomic sequences observed tasks us a family of solutions that learn effectively weak! Neural network policies map directly from raw pixel inputs increases up to challenging problems solve such tasks and discover communication. That uses hand-crafted low-dimensional policy representations, our method offers substantial improvements in exploration efficiency when compared with standard! Dqn, achieves the best realtime agents thus far right pharmacological candidate as a benchmark for deep RL activity... Steps, see Sutton & Barto ( 1998 ) for an introduction respectively:.! Of using deep reinforcement learning inspired by advantage learning. to fall within a local optimum during learning! With some algorithmic im-, provements, leads to significant improvements over the single-stream baselines of Mnih et al,! You need to help your work DQN ) to address this challenge, we empirically show that is! Ziyu, et al 1 ), but uses already published algorithms 100 starting points sampled from a expert.