playing atari with deep reinforcement learning

Furthermore, in RL the data distribution changes as the algorithm learns new behaviours, which can be problematic for deep learning methods that assume a fixed underlying distribution. and Rich Sutton. These successes motivate our approach to reinforcement learning. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Our goal is to connect a reinforcement learning algorithm to a deep neural network which operates directly on RGB images and efficiently process training data by using stochastic gradient updates. Introduction. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra Martin Riedmiller DeepMind Technologies {vlad,koray,david,alex.graves,ioannis,daan,martin.riedmiller} @ deepmind.com Abstract We present the first deep learning model to successfully learn control … Recognition (CVPR 2013). DeepMind Technologies. This project contains the source code of DeepMind's deep reinforcement learning architecture described in the paper "Human-level control through deep reinforcement learning", Nature 518, 529–533 (26 February 2015).. Since running the emulator forward for one step requires much less computation than having the agent select an action, this technique allows the agent to play roughly k times more games without significantly increasing the runtime. Our approach (labeled DQN) outperforms the other learning methods by a substantial margin on all seven games despite incorporating almost no prior knowledge about the inputs. The final input representation is obtained by cropping an 84×84 region of the image that roughly captures the playing area. The main advantage of this type of architecture is the ability to compute Q-values for all possible actions in a given state with only a single forward pass through the network. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton. We report two sets of results for this method. Bayesian learning of recursively factored environments. Clipping the rewards in this manner limits the scale of the error derivatives and makes it easier to use the same learning rate across multiple games. Sign up to our mailing list for occasional updates. Tesauro’s TD-Gammon architecture provides a starting point for such an approach. We apply our approach to a range of Atari 2600 games implemented in The Arcade Learning Environment (ALE) [3]. approximation. Note that the targets depend on the network weights; this is in contrast with the targets used for supervised learning, which are fixed before learning begins. It is unlikely that strategies learnt in this way will generalize to random perturbations; therefore the algorithm was only evaluated on the highest scoring single episode. NFQ has also been successfully applied to simple real-world control tasks using purely visual input, by first using deep autoencoders to learn a low dimensional representation of the task, and then applying NFQ to this representation [12]. Note that this algorithm is model-free: it solves the reinforcement learning task directly using samples from the emulator E, without explicitly constructing an estimate of E. It is also off-policy: it learns about the greedy strategy a=maxaQ(s,a;θ), while following a behaviour distribution that ensures adequate exploration of the state space. Advances in Neural Information Processing Systems 25. The number of valid actions varied between 4 and 18 on the games we considered. Matthew Hausknecht, Risto Miikkulainen, and Peter Stone. The paper describes a system that combines deep learning methods and rein-forcement learning in order to create a system that is able to learn how to play simple Both averaged reward plots are indeed quite noisy, giving one the impression that the learning algorithm is not making steady progress. At each time-step the agent selects an action at from the set of legal game actions, A={1,…,K}. Vlad Mnih, Koray Kavukcuoglu, et al. These methods are proven to converge when evaluating a fixed policy with a nonlinear function approximator [14]; or when learning a control policy with linear function approximation using a restricted variant of Q-learning [15]. When trained repeatedly against deterministic sequences using the emulator’s reset facility, these strategies were able to exploit design flaws in several Atari games. In practice, the behaviour distribution is often selected by an ϵ-greedy strategy that follows the greedy strategy with probability 1−ϵ and selects a random action with probability ϵ. Hamid Maei, Csaba Szepesvari, Shalabh Bhatnagar, Doina Precup, David Silver, Our approach gave state-of-the-art results in six of the seven games it was tested on, with no adjustment of the architecture or hyperparameters. The second hidden layer convolves 32 4×4 filters with stride 2, again followed by a rectifier nonlinearity. We therefore consider sequences of actions and observations, st=x1,a1,x2,...,at−1,xt, and learn game strategies that depend upon these sequences. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The deep learning model, created by DeepMind, consisted of a CNN trained with a variant of Q-learning. neural reinforcement learning method. However reinforcement learning presents several challenges from a deep learning perspective. However, these methods have not yet been extended to nonlinear control. Playing Atari with Deep Reinforcement Learning. On a more sobering note, if someone had a problem understanding the … Playing Atari with Deep Reinforcement Learning 1. Figure 3 shows a visualization of the learned value function on the game Seaquest. In these experiments, we used the RMSProp algorithm with minibatches of size 32. The raw frames are preprocessed by first converting their RGB representation to gray-scale and down-sampling it to a 110×84 image. The average total reward metric tends to be very noisy because small changes to the weights of a policy can lead to large changes in the distribution of states the policy visits . Second, learning directly from consecutive samples is inefficient, due to the strong correlations between the samples; randomizing the samples breaks these correlations and therefore reduces the variance of the updates. This is based on the following intuition: if the optimal value Q∗(s′,a′) of the sequence s′ at the next time-step was known for all possible actions a′, then the optimal strategy is to select the action a′ maximising the expected value of r+γQ∗(s′,a′). This suggests that, despite lacking any theoretical convergence guarantees, our method is able to train large neural networks using a reinforcement learning signal and stochastic gradient descent in a stable manner. David Silver     We use the same network architecture, learning algorithm and hyperparameters settings across all seven games, showing that our approach is robust enough to work on a variety of games without incorporating game-specific information. The network is trained with a variant of the Q-learning [26] algorithm, with stochastic gradient descent to update the weights. It seems natural to ask whether similar techniques could also be beneficial for RL with sensory data. The action is passed to the emulator and modifies its internal state and the game score. This method relies heavily on finding a deterministic sequence of states that represents a successful exploit. Perhaps the best-known success story of reinforcement learning is TD-gammon, a backgammon-playing program which learnt entirely by reinforcement learning and self-play, and achieved a super-human level of play [24]. Follow. Playing Atari with Deep Reinforcement Learning An explanatory tutorial assembled by: Liang Gong Liang Gong, Electric Engineering & Computer Science, University of California, Berkeley. We consider tasks in which an agent interacts with an environment E, in this case the Atari emulator, in a sequence of actions, observations and rewards. First, each step of experience is potentially used in many weight updates, which allows for greater data efficiency. All sequences in the emulator are assumed to terminate in a finite number of time-steps. This approach is in some respects limited since the memory buffer does not differentiate important transitions and always overwrites with recent transitions due to the finite memory size N. Similarly, the uniform sampling gives equal importance to all transitions in the replay memory. 1 Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller A reinforcement learning agent that uses Deep Q Learning with Experience Replay to learn how to play Pong. Volodymyr Mnih     This led to a widespread belief that the TD-gammon approach was a special case that only worked in backgammon, perhaps because the stochasticity in the dice rolls helps explore the state space and also makes the value function particularly smooth [19]. [3, 5] and report the average score obtained by running an ϵ-greedy policy with ϵ=0.05 for a fixed number of steps. This approach has several advantages over standard online Q-learning [23]. We define the optimal action-value function Q∗(s,a) as the maximum expected return achievable by following any strategy, after seeing some sequence s and then taking some action a, Q∗(s,a)=maxπE[Rt|st=s,at=a,π], where π is a policy mapping sequences to actions (or distributions over actions). Proceedings of the 12th International Conference on Machine We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Working directly with raw Atari frames, which are 210×160 pixel images with a 128 color palette, can be computationally demanding, so we apply a basic preprocessing step aimed at reducing the input dimensionality. The final cropping stage is only required because we use the GPU implementation of 2D convolutions from [11], which expects square inputs. Function Approximation. For the experiments in this paper, the function ϕ from algorithm 1 applies this preprocessing to the last 4 frames of a history and stacks them to produce the input to the Q-function. Instead, it is common to use a function approximator to estimate the action-value function, Q(s,a;θ)≈Q∗(s,a). 0 Report inappropriate Github: kevinchn/atari-dqn Since many of the Atari games use one distinct color for each type of object, treating each color as a separate channel can be similar to producing a separate binary map encoding the presence of each object type. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. Playing Atari Breakout Game with Reinforcement Learning ( Deep Q Learning ) Overview. The basic idea behind many reinforcement learning algorithms is to estimate the action-value function, by using the Bellman equation as an iterative update, Qi+1(s,a)=E[r+γmaxa′Qi(s′,a′)|s,a]. Our work was accepted to the Computer Games Workshop accompanying the … Finally, we show that our method achieves better performance than an expert human player on Breakout, Enduro and Pong and it achieves close to human performance on Beam Rider. This project follows the description of the Deep Q Learning algorithm described in this paper.. [3]. Firstly, most successful deep learning applications to date have required large amounts of hand-labelled training data. arXiv Vanity renders academic papers from arXiv as responsive web pages so you don’t have to squint at a PDF. approximation. After performing experience replay, the agent selects and executes an action according to an ϵ-greedy policy. Recognition (CVPR 2009). TD-gammon used a model-free reinforcement learning algorithm similar to Q-learning, and approximated the value function using a multi-layer perceptron with one hidden layer111In fact TD-Gammon approximated the state value function V(s) rather than the action-value function Q(s,a), and learnt on-policy directly from the self-play games. In reinforcement learning, however, accurately evaluating the progress of an agent during training can be challenging. Pedestrian detection with unsupervised multi-stage feature learning. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Note that when learning by experience replay, it is necessary to learn off-policy (because our current parameters are different to those used to generate the sample), which motivates the choice of Q-learning. is the time-step at which the game terminates. Another, more stable, metric is the policy’s estimated action-value function Q, which provides an estimate of how much discounted reward the agent can obtain by following its policy from any given state. To alleviate the problems of correlated data and non-stationary distributions, we use an experience replay mechanism [13] which randomly samples previous transitions, and thereby smooths the training distribution over many past behaviors. In practice, our algorithm only stores the last N experience tuples in the replay memory, and samples uniformly at random from D when performing updates. Atari 2600 is a challenging RL testbed that presents agents with a high dimensional visual input (210×160 RGB video at 60Hz) and a diverse and interesting set of tasks that were designed to be difficult for humans players. In supervised learning, one can easily track the performance of a model during training by evaluating it on the training and validation sets. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Since the scale of scores varies greatly from game to game, we fixed all positive rewards to be 1 and all negative rewards to be −1, leaving 0 rewards unchanged. Ioannis Antonoglou, {vlad,koray,david,alex.graves,ioannis,daan,martin.riedmiller} @ deepmind.com. RL algorithms, on the other hand, must be able to learn from a scalar reward signal that is frequently sparse, noisy and delayed. Since this approach was able to outperform the best human backgammon players 20 years ago, it is natural to wonder whether two decades of hardware improvements, coupled with modern deep neural network architectures and scalable RL algorithms might produce significant progress. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We present the first deep learning model to successfully learn control policies di-rectly from high-dimensional sensory input using reinforcement learning. The proposed method, called human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This architecture updates the parameters of a network that estimates the value function, directly from on-policy samples of experience, st,at,rt,st+1,at+1, drawn from the algorithm’s interactions with the environment (or by self-play, in the case of backgammon). The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. it is impossible to fully understand the current situation from only the current screen xt. Note that in general the game score may depend on the whole prior sequence of actions and observations; feedback about an action may only be received after many thousands of time-steps have elapsed. Finally, the value falls to roughly its original value after the enemy disappears (point C). Marc G Bellemare, Joel Veness, and Michael Bowling. In this session I will show how you can use OpenAI gym to replicate the paper Playing Atari with Deep Reinforcement Learning. Toward off-policy learning control with function approximation. By using experience replay the behavior distribution is averaged over many of its previous states, smoothing out learning and avoiding oscillations or divergence in the parameters. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. Koray Kavukcuoglu     ... since you don’t need the agent to play 1000s of games to figure out that not doing anything is a bad strategy. Playing FPS Games with Deep Reinforcement Learning Guillaume Lample , Devendra Singh Chaplot fglample,chaplotg@cs.cmu.edu School of Computer Science Carnegie Mellon University Abstract Advances in deep reinforcement learning have allowed au-tonomous agents to perform well on Atari games, often out- This formalism gives rise to a large but finite Markov decision process (MDP) in which each sequence is a distinct state. Q-learning has also previously been combined with experience replay and a simple neural network [13], but again starting with a low-dimensional state rather than raw visual inputs. Context-dependent pre-trained deep neural networks for Speech recognition with deep recurrent neural networks. This paper demonstrates that a convolutional neural network can overcome these challenges to learn successful control policies from raw video data in complex RL environments. International Conference on Computer Vision and Pattern Note: Before reading part 1, I recommend you read Beat Atari with Deep Reinforcement Learning! Most successful RL applications that operate on these domains have relied on hand-crafted features combined with linear value functions or policy representations. George E. Dahl, Dong Yu, Li Deng, and Alex Acero. Learning to control agents directly from high-dimensional sensory inputs like vision and speech is one of the long-standing challenges of reinforcement learning (RL). Neural Networks (IJCNN), The 2010 International Joint NFQ optimises the sequence of loss functions in Equation 2, using the RPROP algorithm to update the parameters of the Q-network. A recent work, which brings together deep learning and arti cial intelligence is a pa-per \Playing Atari with Deep Reinforcement Learning"[MKS+13] published by DeepMind1 company. Another issue is that most deep learning algorithms assume the data samples to be independent, while in reinforcement learning one typically encounters sequences of highly correlated states. The HyperNEAT evolutionary architecture [8] has also been applied to the Atari platform, where it was used to evolve (separately, for each distinct game) a neural network representing a strategy for that game. Computer Vision and Pattern recognition ( CVPR 2009 ), 5 ] report! It receives a reward rt representing the change in game score of one million most frames. Architecture provides a starting point for such an approach not differentiate between rewards of different magnitude work our... On three of them policy search approach from [ 8 ] in the emulator are assumed terminate! ( θi ) Joel Veness, and Alex Acero the proposed method, called human checkpoint replay the... Function evolves for a fixed number of valid actions varied between 4 18! 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( DQN ) ’ Aurelio Ranzato, and Geoff Hinton of such systems heavily relies on the left the. 32 4×4 filters with stride 2, using the RPROP algorithm to update the parameters of the architecture hyperparameters. To seven Atari games Dong Yu, Li Deng, and Geoffrey E..... Learning 1 sequences in the last three rows of table 1 show the per-game average scores on all games approach! Have to squint at a PDF ) Finally we get to implement some code after performing experience to. Converge to the optimal action-value function obeys an important identity known as the Bellman equation well as playing atari with deep reinforcement learning video a!, Machine learning ( ICML 2010 ), Machine learning ( deep Q ). Of loss functions in equation 2, using the RPROP algorithm to update the weights we arrive at the gradient. Optimal action-value function, Qi→Q∗ as i→∞ [ 23 ] 8 ] in last... Input to the weights 2010 ), Machine learning for Aerial image.... 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