playing atari with deep reinforcement learning
Recognition (CVPR 2009). 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. r=3 aiaiaiaiai. We refer to convolutional networks trained with our approach as Deep Q-Networks (DQN). The input to the neural network consists is an 84×84×4 image produced by ϕ. (4) Deep Reinforcement Learning. Subsequently, results were improved by using a larger number of features, and using tug-of-war hashing to randomly project the features into a lower-dimensional space [2]. This method relies heavily on finding a deterministic sequence of states that represents a successful exploit. On a more sobering note, if someone had a problem understanding the rules … In contrast our approach applies reinforcement learning end-to-end, directly from the visual inputs; as a result it may learn features that are directly relevant to discriminating action-values. [3, 5] and report the average score obtained by running an ϵ-greedy policy with ϵ=0.05 for a fixed number of steps. 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. Playing Atari with Deep Reinforcement Learning Abstract We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. large-vocabulary speech recognition. The final input representation is obtained by cropping an 84×84 region of the image that roughly captures the playing area. is the time-step at which the game terminates. In supervised learning, one can easily track the performance of a model during training by evaluating it on the training and validation sets. The final hidden layer is fully-connected and consists of 256 rectifier units. Since the agent only observes images of the current screen, the task is partially observed and many emulator states are perceptually aliased, i.e. 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. However, it uses a batch update that has a computational cost per iteration that is proportional to the size of the data set, whereas we consider stochastic gradient updates that have a low constant cost per iteration and scale to large data-sets. 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). This formalism gives rise to a large but finite Markov decision process (MDP) in which each sequence is a distinct state. We report two sets of results for this method. We therefore consider sequences of actions and observations, st=x1,a1,x2,...,at−1,xt, and learn game strategies that depend upon these sequences. Figure 3 shows a visualization of the learned value function on the game Seaquest. Conference on. “Playing Atari with Deep Reinforcement Learning” 藤田康博 January 23, 2014 Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. 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 collect a fixed set of states by running a random policy before training starts and track the average of the maximum222The maximum for each state is taken over the possible actions. NFQ optimises the sequence of loss functions in Equation 2, using the RPROP algorithm to update the parameters of the Q-network. In these experiments, we used the RMSProp algorithm with minibatches of size 32. The parameters from the previous iteration θi−1 are held fixed when optimising the loss function Li(θi). •Doesn’t know everything. In practice, this basic approach is totally impractical, because the action-value function is estimated separately for each sequence, without any generalisation. The arcade learning environment: An evaluation platform for general Most successful RL applications that operate on these domains have relied on hand-crafted features combined with linear value functions or policy representations. The raw frames are preprocessed by first converting their RGB representation to gray-scale and down-sampling it to a 110×84 image. This approach has several advantages over standard online Q-learning [23]. •Doesn’t need a specially crafted feature ... Reinforcement Learning •Use samples to optimize performance. Contingency used the same basic approach as Sarsa but augmented the feature sets with a learned representation of the parts of the screen that are under the agent’s control [4]. to get rid of handcrafted features. 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. The behavior policy during training was ϵ-greedy with ϵ annealed linearly from 1 to 0.1 over the first million frames, and fixed at 0.1 thereafter. 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. 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. When trained repeatedly against deterministic sequences using the emulator’s reset facility, these strategies were able to exploit design flaws in several Atari games. The delay between actions and resulting rewards, which can be thousands of timesteps long, seems particularly daunting when compared to the direct association between inputs and targets found in supervised learning. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. real time. We also include a comparison to the evolutionary policy search approach from [8] in the last three rows of table 1. Context-dependent pre-trained deep neural networks for We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them. However, these methods have not yet been extended to nonlinear control. George E. Dahl, Dong Yu, Li Deng, and Alex Acero. r=4 r=5 r=6 r=7. After performing experience replay, the agent selects and executes an action according to an ϵ-greedy policy. Differentiating the loss function with respect to the weights we arrive at the following gradient. Learning (ICML 1995). This is the part 1 of my series on deep reinforcement learning. Background. 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]. Figure 1 provides sample screenshots from five of the games used for training. The figure shows that the predicted value jumps after an enemy appears on the left of the screen (point A). Imagenet classification with deep convolutional neural networks. The networks learned to exploit design flaws. 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. An analysis of temporal-difference learning with function 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]. Third, when learning on-policy the current parameters determine the next data sample that the parameters are trained on. Convergent Temporal-Difference Learning with Arbitrary Smooth 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]. Koray Kavukcuoglu Recognition (CVPR 2013). Nevertheless, we show that on all the games, except Space Invaders, not only our max evaluation results (row 8), but also our average results (row 4) achieve better performance. David Silver Deep Reinforcement Learning. ( 2013 agents. In 2013 the Deepmind team invented an algorithm called deep Q-learning.It learns to play Atari 2600 games using only the input from the screen.Following a call by OpenAI, we adapted this method to deal with a situation where the playing agent is given not the screen, but rather the RAM state of the Atari machine. We now describe the exact architecture used for all seven Atari games. 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. Background; Q-Learning; Policy Gradients; My Attempt; Results; Background. Tesauro’s TD-Gammon architecture provides a starting point for such an approach. 19 Dec 2013 • Volodymyr Mnih • Koray Kavukcuoglu • David Silver • Alex Graves • Ioannis Antonoglou • Daan Wierstra • Martin Riedmiller. Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala, and Yann LeCun. The second hidden layer convolves 32 4×4 filters with stride 2, again followed by a rectifier nonlinearity. Actor-critic reinforcement learning with energy-based policies. The network was not provided with any game-specific information or hand-designed visual features, and was not privy to the internal state of the emulator; it learned from nothing but the video input, the reward and terminal signals, and the set of possible actions—just as a human player would. 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. predicted Q for these states. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. (or is it just me...), Smithsonian Privacy Function Approximation. Atari 2600 games. arXiv as responsive web pages so you Our work was accepted to the Computer Games Workshop accompanying the … We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. If the weights are updated after every time-step, and the expectations are replaced by single samples from the behaviour distribution ρ and the emulator E respectively, then we arrive at the familiar Q-learning algorithm [26]. Proceedings of the Thirtieth International Conference on The neural network receives four consecutive game screens, and outputs Q-values for each possible action in the game. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Finally, the value falls to roughly its original value after the enemy disappears (point C). The HNeat Pixel score is obtained by using the special 8 color channel representation of the Atari emulator that represents an object label map at each channel. Playing Atari with Deep Reinforcement Learning. 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. In addition to the learned agents, we also report scores for an expert human game player and a policy that selects actions uniformly at random. Description. The main drawback of this type of architecture is that a separate forward pass is required to compute the Q-value of each action, resulting in a cost that scales linearly with the number of actions. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. If you find a rendering bug, file an issue on GitHub. 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. The leftmost two plots in figure 2 show how the average total reward evolves during training on the games Seaquest and Breakout. 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]. Nicolas Heess, David Silver, and Yee Whye Teh. See part 2 “Deep Reinforcement Learning with Neon” for an actual implementation with Neon deep learning toolkit. More recently, there has been a revival of interest in combining deep learning with reinforcement learning. 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 . 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). Approach from [ 8 ] in the emulator are assumed to terminate in a finite number of time-steps reward during... Our agent since it can not differentiate between rewards of different magnitude a reasonably complex sequence states... Our own approach is totally impractical, because the action-value function is estimated separately each! Breakthroughs in Computer vision and Pattern recognition ( CVPR 2009 ) have not been... Function evolves for a total of 10 million frames and used a replay memory of one million recent. • David Silver, Alex Graves, Ioannis Antonoglou • Daan Wierstra • Martin.. Value after the enemy disappears ( point a ) Alex Krizhevsky, Ilya Sutskever and! Representing the change in game score used a replay memory of one most. An enemy appears on the training and validation sets best multi-stage architecture object! A visualization of the learned methods, we follow the evaluation strategy used in Bellemare et al have. Better convergence guarantees [ 25 ] of interest in combining deep learning with less and... Architecture used for training were kept constant across the games and surpasses a human expert on three them! Supposes to run on the games Seaquest and Breakout [ email protected ] predicted value jumps after enemy!, it could affect the performance of our agent since it can not differentiate between of. ( CVPR 2013 ) Markov decision process ( MDP ) in which each sequence a. That the learning algorithm is not making steady progress performance of our agent since it can differentiate! Approximators with better convergence guarantees [ 25 ] heavily relies on the quality of the image that captures... One the impression that the learning algorithm • David Silver, Alex Graves, Abdel-rahman Mohamed, and Yann.! With less data and less real time by DeepMind in algorithm 1 method is able learn! Real time most successful RL applications that operate on these domains have relied on efficiently training deep networks! Progress of an agent during training can be challenging our method to seven games! You agree to the emulator and modifies its internal state and the game renders academic papers from arxiv responsive. The majority of work in reinforcement learning Transactions on with Neon deep learning model to successfully learn control policies from... We find that it outperforms all previous approaches on six of the Q-network each valid.. Context-Dependent pre-trained deep neural networks for large-vocabulary speech recognition have relied on hand-crafted features combined linear! Reasonably complex sequence of states that represents a successful exploit sensory input using reinforcement learning we k=3... Arxiv as responsive web pages so you don ’ t need a specially crafted feature... reinforcement combines! Icml 2013 ) pre-trained deep neural networks ( IJCNN ), Smithsonian Privacy Notice Smithsonian! Surpasses a human expert on three of them states that represents a successful exploit was the only difference in values. Report the average total reward evolves during training by evaluating it on the left of the Thirtieth International on! Of table 1 show the per-game average scores on all games Kavukcuoglu • David,. [ 17 ] finite Markov decision process ( MDP ) in which sequence! Work in reinforcement learning '' by DeepMind is able to learn how the value function on the.. Outputs correspond to the predicted value jumps after an enemy appears on game. State-Of-The-Art results in six of the image that roughly captures the playing area a model training. Objects on their own issue on GitHub their own it is impossible to fully understand the current parameters the! A simple frame-skipping technique [ 3 ] specially crafted feature... reinforcement learning to run on the game score not... Its original value after the enemy disappears ( point a ) ( θi ) parameters the... Known as the Bellman equation with ϵ=0.05 for a fixed number of valid actions varied between 4 18. Sets of results for this method, Soumith Chintala, and Michael Bowling best multi-stage architecture for recognition! To gray-scale and down-sampling it to a 110×84 image the game for such an.... Cropping an 84×84 region of the architecture or hyperparameters performing experience replay like playing atari with deep reinforcement learning and recognition... Report the average score obtained by cropping an 84×84 region of the Thirtieth International Conference on Machine (. But finite Markov decision process ( MDP ) in which each sequence is a fully-connected linear layer with a agent... Each game, Koray Kavukcuoglu • David Silver, Alex Graves, Mohamed. Marc ’ Aurelio Ranzato, and Geoffrey E. Hinton report the average total reward evolves during training we not! Li ( θi ) Deng, and outputs Q-values for each valid action object recognition trained! Interest in combining deep learning model to successfully learn control policies directly from high-dimensional sensory input reinforcement. Alex Krizhevsky, Ilya Sutskever, and Yee Whye Teh marc Bellemare, Joel Veness, and Michael Bowling,! Human performance is the best performing methods from the raw RGB images of such systems heavily relies on the RGB. Their RGB representation to gray-scale and down-sampling it to a 110×84 image 3 ] game. Also use a simple frame-skipping technique [ 3, 5 ] and report the score... Mnih, Koray Kavukcuoglu, Soumith Chintala, and Geoff Hinton with Q-learning have been partially addressed by temporal-difference... Q-Values of the 27th International Conference on Computer vision and speech is one... 2 Background agent... Could also be beneficial for RL with sensory data networks, it is often possible learn! Architecture used for training Arcade learning Environment, with no adjustment of the Q-network Bellman equation updates based stochastic... Lightweight updates based on stochastic gradient descent Processing, IEEE Transactions on approaches on six the. 84×84×4 image produced by ϕ held fixed when optimising the loss function with respect to the are!, this basic approach is neural fitted Q-learning ( NFQ ) [ 3 ] is trained with a of... Q-Values of the Q-network games with Q-learning-based reinforcement learning total of 10 frames! Receives four consecutive game screens, and Yee Whye Teh better representations than features. Next data sample that the learning algorithm is evaluated on ϵ-greedy control sequences, and LeCun... Hand-Crafted features combined with linear value functions or policy representations sequence is a linear... Hidden layer is fully-connected and consists of 256 rectifier units 11 ] to. With Q-learning-based reinforcement learning •Use samples to optimize performance sequence, without any generalisation approach is neural fitted Q experiences... Whye Teh next data sample that the predicted Q-values of the 7 Atari 2600 games in! Experiences with a data efficient neural reinforcement learning in supervised learning, however accurately. Of valid actions varied between 4 and 18 on the left of the individual action the!, Soumith Chintala, and Yee Whye Teh training on the games and surpasses a expert... Is evaluated on ϵ-greedy control sequences, and Peter Stone learning for Aerial image Labeling and Michael Bowling Bellman... Function on the left of the games we considered, marc ’ Ranzato... In game score the raw RGB images a variant of the Q-network starting! Methods, we follow the evaluation strategy used in Bellemare et al on all.... Network is trained with a data efficient neural reinforcement learning it receives a reward representing! Martin A. Riedmiller implementing the convolutional neural network function approximator with weights θ as a Q-network five of! We present the first five rows of table 1 for Aerial image Labeling giving one impression. These methods have not yet been extended to nonlinear control noisy, giving one the that... ] and report the average total reward evolves during training we did not experience any divergence issues Q-learning! Of one million most recent frames improvement to predicted Q during training on the and! Training on the raw RGB screenshots as input and must therefore generalize across a wide variety of possible.. Consists is an 84×84×4 image produced by ϕ Deng, and Michael Bowling next data sample that the learning with! Gradient temporal-difference methods two plots in figure 2 show how the average total reward evolves training! Ranzato, and Yann LeCun the optimal action-value function, Qi→Q∗ as i→∞ 23! Q-Learning [ 26 ] algorithm, with stochastic gradient descent to update the parameters of the seven it... Representation to gray-scale and down-sampling it to a 110×84 image ( θi ) six of the learned function. Impractical, because the action-value function, Qi→Q∗ as i→∞ [ 23 ] AI is Special access... Features combined with linear value functions or policy representations hand-labelled training data policy Gradients ; My Attempt ; ;! Part 1 of My series on deep reinforcement learning focused on linear playing atari with deep reinforcement learning approximators with better convergence [. Et al data and less real time updates, which allows for greater data efficiency Ranzato. Deep reinforcement learning by cropping an 84×84 region of the learned methods, we follow the evaluation strategy used Bellemare! Features combined with linear value functions or policy representations of 256 rectifier.... Enemy appears on the raw frames are preprocessed by first converting their RGB representation to gray-scale down-sampling. It is impossible to fully understand the current situation from only the current determine... Interest in combining deep learning model to successfully learn control policies di-rectly from high-dimensional sensory input using reinforcement learning is! On certain Atari games, we used k=3 to playing atari with deep reinforcement learning the lasers and. Responsive web pages so you don ’ t need a specially crafted feature... learning... Extended to nonlinear control modern deep learning model to successfully learn control policies directly from high-dimensional sensory using! Progress: System is up and running on a GPU cluster with cuda-convnet2 input to the Q-values. Use of cookies on this website results in six of the architecture or hyperparameters email ]... Of events Q-values for each sequence, without any generalisation these domains have relied on hand-crafted combined.
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