Playing Atari with Deep Reinforcement Learning
Volodymyr Kavukcuoglu David Wierstra Martin Riedmiller
DeepMind Technologies {vlad,koray,david,alex.graves,ioannis,daan,martin.riedmiller} @ deepmind.com
We present the first deep learning model to successfully learn control policies di- rectly from high-dimensional sensory input using reinforcement learning. 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 apply our method to seven Atari 2600 games from the Arcade Learn- ing Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
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1 Introduction
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). Most successful RL applica- tions that operate on these domains have relied on hand-crafted features combined with linear value functions or policy representations. Clearly, the performance of such systems heavily relies on the quality of the feature representation.
Recent advances in deep learning have made it possible to extract high-level features from raw sen- sory data, leading to breakthroughs in computer vision [11, 22, 16] and speech recognition [6, 7]. These methods utilise a range of neural network architectures, including convolutional networks, multilayer perceptrons, restricted Boltzmann machines and recurrent neural networks, and have ex- ploited both supervised and unsupervised learning. It seems natural to ask whether similar tech- niques could also be beneficial for RL with sensory data.
However reinforcement learning presents several challenges from a deep learning perspective. Firstly, most successful deep learning applications to date have required large amounts of hand- labelled training data. RL algorithms, on the other hand, must be able to learn from a scalar reward signal that is frequently sparse, noisy and delayed. 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. 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. Furthermore, in RL the data distribu- tion changes as the algorithm learns new behaviours, which can be problematic for deep learning methods that assume a fixed underlying distribution.
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. The network is trained with a variant of the Q-learning [26] algorithm, with stochastic gradient descent to update the weights. To alleviate the problems of correlated data and non-stationary distributions, we use
Figure1: ScreenshotsfromfiveAtari2600Games:(Left-to-right)Pong,Breakout,SpaceInvaders, Seaquest, Beam Rider
an experience replay mechanism [13] which randomly samples previous transitions, and thereby smooths the training distribution over many past behaviors.
We apply our approach to a range of Atari 2600 games implemented in The Arcade Learning Envi- ronment (ALE) [3]. Atari 2600 is a challenging RL testbed that presents agents with a high dimen- sional 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. Our goal is to create a single neural network agent that is able to successfully learn to play as many of the games as possible. The network was not pro- vided 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. Furthermore the network ar- chitecture and all hyperparameters used for training were kept constant across the games. So far the network has outperformed all previous RL algorithms on six of the seven games we have attempted and surpassed an expert human player on three of them. Figure 1 provides sample screenshots from five of the games used for training.
2 Background
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. At each time-step the agent selects an action at from the set of legal game actions, A = {1, . . . , K }. The action is passed to the emulator and modifies its internal state and the game score. In general E may be stochastic. The emulator’s internal state is not observed by the agent; instead it observes an image xt ∈ Rd from the emulator, which is a vector of raw pixel values representing the current screen. In addition it receives a reward rt representing the change in game score. 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.
Since the agent only observes images of the current screen, the task is partially observed and many emulator states are perceptually aliased, i.e. it is impossible to fully understand the current situation from only the current screen xt. We therefore consider sequences of actions and observations, st = x1, a1, x2, …, at−1, xt, and learn game strategies that depend upon these sequences. All sequences in the emulator are assumed to terminate in a finite number of time-steps. This formalism gives rise to a large but finite Markov decision process (MDP) in which each sequence is a distinct state. As a result, we can apply standard reinforcement learning methods for MDPs, simply by using the complete sequence st as the state representation at time t.
The goal of the agent is to interact with the emulator by selecting actions in a way that maximises future rewards. We make the standard assumption that future rewards are discounted by a factor of
T t′−t γ per time-step, and define the future discounted return at time t as Rt = t′ =t γ
rt′ , where T is the time-step at which the game terminates. 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) = maxπ E [Rt|st = s, at = a, π], where π is a policy
mapping sequences to actions (or distributions over actions).
The optimal action-value function obeys an important identity known as the Bellman equation. 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′),
Q (s,a)=Es′∼E r+γmaxQ (s,a)s,a (1)
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 + γ maxa′ Qi (s′ , a′ )|s, a]. Such value iteration algorithms converge to the optimal action- value function, Qi → Q∗ as i → ∞ [23]. In practice, this basic approach is totally impractical, because the action-value function is estimated separately for each sequence, without any generali- sation. Instead, it is common to use a function approximator to estimate the action-value function, Q(s, a; θ) ≈ Q∗(s, a). In the reinforcement learning community this is typically a linear function approximator, but sometimes a non-linear function approximator is used instead, such as a neural network. We refer to a neural network function approximator with weights θ as a Q-network. A Q-network can be trained by minimising a sequence of loss functions Li(θi) that changes at each iteration i,
Li (θi) = Es,a∼ρ(·) (yi − Q (s, a; θi)) , (2)
where yi = Es′∼E [r + γ maxa′ Q(s′, a′; θi−1)|s, a] is the target for iteration i and ρ(s, a) is a probability distribution over sequences s and actions a that we refer to as the behaviour distribution. The parameters from the previous iteration θi−1 are held fixed when optimising the loss function Li (θi). 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. Differentiating the loss function with respect to the weights we arrive at the following gradient,
′′ ∇θiLi(θi)=Es,a∼ρ(·);s′∼E r+γmaxQ(s,a;θi−1)−Q(s,a;θi) ∇θiQ(s,a;θi) . (3)
Rather than computing the full expectations in the above gradient, it is often computationally expe- dient to optimise the loss function by stochastic gradient descent. 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].
Note that this algorithm is model-free: it solves the reinforcement learning task directly using sam- ples from the emulator E, without explicitly constructing an estimate of E. It is also off-policy: it learns about the greedy strategy a = maxa Q(s, a; θ), while following a behaviour distribution that ensures adequate exploration of the state space. In practice, the behaviour distribution is often se- lected by an ε-greedy strategy that follows the greedy strategy with probability 1 − ε and selects a random action with probability ε.
3 Related Work
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]. 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 layer1 .
However, early attempts to follow up on TD-gammon, including applications of the same method to chess, Go and checkers were less successful. 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].
Furthermore, it was shown that combining model-free reinforcement learning algorithms such as Q- learning with non-linear function approximators [25], or indeed with off-policy learning [1] could cause the Q-network to diverge. Subsequently, the majority of work in reinforcement learning fo- cused on linear function approximators with better convergence guarantees [25].
1In 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
More recently, there has been a revival of interest in combining deep learning with reinforcement learning. Deep neural networks have been used to estimate the environment E; restricted Boltzmann machines have been used to estimate the value function [21]; or the policy [9]. In addition, the divergence issues with Q-learning have been partially addressed by gradient temporal-difference methods. 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]. However, these methods have not yet been extended to nonlinear control.
Perhaps the most similar prior work to our own approach is neural fitted Q-learning (NFQ) [20]. NFQ optimises the sequence of loss functions in Equation 2, using the RPROP algorithm to update the parameters of the Q-network. 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. 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]. 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. 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.
The use of the Atari 2600 emulator as a reinforcement learning platform was introduced by [3], who applied standard reinforcement learning algorithms with linear function approximation and generic visual features. 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]. 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. When trained repeatedly against deterministic sequences using the emulator’s reset facility, these strategies were able to exploit design flaws in several Atari games.
4 Deep Reinforcement Learning
Recent breakthroughs in computer vision and speech recognition have relied on efficiently training deep neural networks on very large training sets. The most successful approaches are trained directly from the raw inputs, using lightweight updates based on stochastic gradient descent. By feeding sufficient data into deep neural networks, it is often possible to learn better representations than handcrafted features [11]. These successes motivate our approach to 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.
Tesauro’s TD-Gammon architecture provides a starting point for such an approach. This architec- ture 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 envi- ronment (or by self-play, in the case of backgammon). 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.
In contrast to TD-Gammon and similar online approaches, we utilize a technique known as expe- rience replay [13] where we store the agent’s experiences at each time-step, et = (st, at, rt, st+1) in a data-set D = e1 , …, eN , pooled over many episodes into a replay memory. During the inner loop of the algorithm, we apply Q-learning updates, or minibatch updates, to samples of experience, e ∼ D, drawn at random from the pool of stored samples. After performing experience replay, the agent selects and executes an action according to an ε-greedy policy. Since using histories of arbitrary length as inputs to a neural network can be difficult, our Q-function instead works on fixed length representation of histories produced by a function φ. The full algorithm, which we call deep Q-learning, is presented in Algorithm 1.
This approach has several advantages over standard online Q-learning [23]. First, each step of experience is potentially used in many weight updates, which allows for greater data efficiency.
Algorithm 1 Deep Q-learning with Experience Replay
Initialize replay memory D to capacity N
Initialize action-value function Q with random weights for episode = 1, M do
Initialise sequence s1 = {x1} and preprocessed sequenced φ1 = φ(s1) for t = 1, T do
With probability ε select a random action at
otherwise select at = maxa Q∗(φ(st), a; θ)
Execute action at in emulator and observe reward rt and image xt+1 Set st+1 = st, at, xt+1 and preprocess φt+1 = φ(st+1)
Store transition (φt, at, rt, φt+1) in D
Sample random minibatch of transitions (φj , aj , rj , φj +1 ) from D
rj for terminal φj+1
Set yj = rj + γ maxa′ Q(φj+1, a′; θ) for non-terminal φj+1
Perform a gradient descent step on (yj − Q(φj , aj ; θ))2 according to equation 3 end for
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. Third, when learning on-policy the current parameters determine the next data sample that the parameters are trained on. For example, if the maximizing action is to move left then the training samples will be dominated by samples from the left-hand side; if the maximizing action then switches to the right then the training distribution will also switch. It is easy to see how unwanted feedback loops may arise and the parameters could get stuck in a poor local minimum, or even diverge catastrophically [25]. 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. 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.
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. 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. A more sophisticated sampling strategy might emphasize transitions from which we can learn the most, similar to prioritized sweeping [17].
4.1 Preprocessing and Model Architecture
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 raw frames are preprocessed by first converting their RGB representation to gray-scale and down-sampling it to a 110×84 image. The final input representation is obtained by cropping an 84 × 84 region of the image that roughly captures the playing area. The final cropping stage is only required because we use the GPU implementation of 2D convolutions from [11], which expects square inputs. 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.
There are several possible ways of parameterizing Q using a neural network. Since Q maps history- action pairs to scalar estimates of their Q-value, the history and the action have been used as inputs to the neural network by some previous approaches [20, 12]. 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 instead use an architecture in which there is a separate output unit for each possible action, and only the state represent
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