## Abstract

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

## Main

All games of perfect information have an optimal value function, *v*^{*}(*s*), which determines the outcome of the game, from every board position or state *s*, under perfect play by all players. These games may be solved by recursively computing the optimal value function in a search tree containing approximately *b*^{d} possible sequences of moves, where *b* is the game’s breadth (number of legal moves per position) and *d* is its depth (game length). In large games, such as chess (*b* ≈ 35, *d* ≈ 80)^{1} and especially Go (*b* ≈ 250, *d* ≈ 150)^{1}, exhaustive search is infeasible^{2,3}, but the effective search space can be reduced by two general principles. First, the depth of the search may be reduced by position evaluation: truncating the search tree at state *s* and replacing the subtree below *s* by an approximate value function *v*(*s*) ≈ *v*^{*}(*s*) that predicts the outcome from state *s*. This approach has led to superhuman performance in chess^{4}, checkers^{5} and othello^{6}, but it was believed to be intractable in Go due to the complexity of the game^{7}. Second, the breadth of the search may be reduced by sampling actions from a policy *p*(*a*|*s*) that is a probability distribution over possible moves *a* in position *s*. For example, Monte Carlo rollouts^{8} search to maximum depth without branching at all, by sampling long sequences of actions for both players from a policy *p*. Averaging over such rollouts can provide an effective position evaluation, achieving superhuman performance in backgammon^{8} and Scrabble^{9}, and weak amateur level play in Go^{10}.

Monte Carlo tree search (MCTS)^{11,12} uses Monte Carlo rollouts to estimate the value of each state in a search tree. As more simulations are executed, the search tree grows larger and the relevant values become more accurate. The policy used to select actions during search is also improved over time, by selecting children with higher values. Asymptotically, this policy converges to optimal play, and the evaluations converge to the optimal value function^{12}. The strongest current Go programs are based on MCTS, enhanced by policies that are trained to predict human expert moves^{13}. These policies are used to narrow the search to a beam of high-probability actions, and to sample actions during rollouts. This approach has achieved strong amateur play^{13,14,15}. However, prior work has been limited to shallow policies^{13,14,15} or value functions^{16} based on a linear combination of input features.

Recently, deep convolutional neural networks have achieved unprecedented performance in visual domains: for example, image classification^{17}, face recognition^{18}, and playing Atari games^{19}. They use many layers of neurons, each arranged in overlapping tiles, to construct increasingly abstract, localized representations of an image^{20}. We employ a similar architecture for the game of Go. We pass in the board position as a 19 × 19 image and use convolutional layers to construct a representation of the position. We use these neural networks to reduce the effective depth and breadth of the search tree: evaluating positions using a value network, and sampling actions using a policy network.

We train the neural networks using a pipeline consisting of several stages of machine learning (Fig. 1). We begin by training a supervised learning (SL) policy network *p*_{σ} directly from expert human moves. This provides fast, efficient learning updates with immediate feedback and high-quality gradients. Similar to prior work^{13,15}, we also train a fast policy *p*_{π} that can rapidly sample actions during rollouts. Next, we train a reinforcement learning (RL) policy network *p*_{ρ} that improves the SL policy network by optimizing the final outcome of games of self-play. This adjusts the policy towards the correct goal of winning games, rather than maximizing predictive accuracy. Finally, we train a value network *v*_{θ} that predicts the winner of games played by the RL policy network against itself. Our program AlphaGo efficiently combines the policy and value networks with MCTS.

## Supervised learning of policy networks

For the first stage of the training pipeline, we build on prior work on predicting expert moves in the game of Go using supervised learning^{13,21,22,23,24}. The SL policy network *p*_{σ}(*a|s*) alternates between convolutional layers with weights *σ*, and rectifier nonlinearities. A final softmax layer outputs a probability distribution over all legal moves *a*. The input *s* to the policy network is a simple representation of the board state (see Extended Data Table 2). The policy network is trained on randomly sampled state-action pairs (*s*, *a*), using stochastic gradient ascent to maximize the likelihood of the human move *a* selected in state *s*

We trained a 13-layer policy network, which we call the SL policy network, from 30 million positions from the KGS Go Server. The network predicted expert moves on a held out test set with an accuracy of 57.0% using all input features, and 55.7% using only raw board position and move history as inputs, compared to the state-of-the-art from other research groups of 44.4% at date of submission^{24} (full results in Extended Data Table 3). Small improvements in accuracy led to large improvements in playing strength (Fig. 2a); larger networks achieve better accuracy but are slower to evaluate during search. We also trained a faster but less accurate rollout policy *p*_{π}(*a*|*s*), using a linear softmax of small pattern features (see Extended Data Table 4) with weights *π*; this achieved an accuracy of 24.2%, using just 2 μs to select an action, rather than 3 ms for the policy network.

## Reinforcement learning of policy networks

The second stage of the training pipeline aims at improving the policy network by policy gradient reinforcement learning (RL)^{25,26}. The RL policy network *p*_{ρ} is identical in structure to the SL policy network, and its weights *ρ* are initialized to the same values, *ρ* = *σ*. We play games between the current policy network *p*_{ρ} and a randomly selected previous iteration of the policy network. Randomizing from a pool of opponents in this way stabilizes training by preventing overfitting to the current policy. We use a reward function *r*(*s*) that is zero for all non-terminal time steps *t* < *T*. The outcome *z*_{t} = ± *r*(*s*_{T}) is the terminal reward at the end of the game from the perspective of the current player at time step *t*: +1 for winning and −1 for losing. Weights are then updated at each time step *t* by stochastic gradient ascent in the direction that maximizes expected outcome^{25}

We evaluated the performance of the RL policy network in game play, sampling each move from its output probability distribution over actions. When played head-to-head, the RL policy network won more than 80% of games against the SL policy network. We also tested against the strongest open-source Go program, Pachi^{14}, a sophisticated Monte Carlo search program, ranked at 2 amateur *dan* on KGS, that executes 100,000 simulations per move. Using no search at all, the RL policy network won 85% of games against Pachi. In comparison, the previous state-of-the-art, based only on supervised learning of convolutional networks, won 11% of games against Pachi^{23} and 12% against a slightly weaker program, Fuego^{24}.

## Reinforcement learning of value networks

The final stage of the training pipeline focuses on position evaluation, estimating a value function *v*^{p}(*s*) that predicts the outcome from position *s* of games played by using policy *p* for both players^{28,29,30}

Ideally, we would like to know the optimal value function under perfect play *v*^{*}(*s*); in practice, we instead estimate the value function for our strongest policy, using the RL policy network *p*_{ρ}. We approximate the value function using a value network *v*_{θ}(*s*) with weights *θ*, . This neural network has a similar architecture to the policy network, but outputs a single prediction instead of a probability distribution. We train the weights of the value network by regression on state-outcome pairs (*s*, *z*), using stochastic gradient descent to minimize the mean squared error (MSE) between the predicted value *v*_{θ}(*s*), and the corresponding outcome *z*

The naive approach of predicting game outcomes from data consisting of complete games leads to overfitting. The problem is that successive positions are strongly correlated, differing by just one stone, but the regression target is shared for the entire game. When trained on the KGS data set in this way, the value network memorized the game outcomes rather than generalizing to new positions, achieving a minimum MSE of 0.37 on the test set, compared to 0.19 on the training set. To mitigate this problem, we generated a new self-play data set consisting of 30 million distinct positions, each sampled from a separate game. Each game was played between the RL policy network and itself until the game terminated. Training on this data set led to MSEs of 0.226 and 0.234 on the training and test set respectively, indicating minimal overfitting. Figure 2b shows the position evaluation accuracy of the value network, compared to Monte Carlo rollouts using the fast rollout policy *p*_{π}; the value function was consistently more accurate. A single evaluation of *v*_{θ}(*s*) also approached the accuracy of Monte Carlo rollouts using the RL policy network *p*_{ρ}, but using 15,000 times less computation.

## Searching with policy and value networks

AlphaGo combines the policy and value networks in an MCTS algorithm (Fig. 3) that selects actions by lookahead search. Each edge (*s*, *a*) of the search tree stores an action value *Q*(*s*, *a*), visit count *N*(*s*, *a*), and prior probability *P*(*s*, *a*). The tree is traversed by simulation (that is, descending the tree in complete games without backup), starting from the root state. At each time step *t* of each simulation, an action *a*_{t} is selected from state *s*_{t}

so as to maximize action value plus a bonus

that is proportional to the prior probability but decays with repeated visits to encourage exploration. When the traversal reaches a leaf node *s*_{L} at step *L*, the leaf node may be expanded. The leaf position *s*_{L} is processed just once by the SL policy network *p*_{σ}. The output probabilities are stored as prior probabilities *P* for each legal action *a*, . The leaf node is evaluated in two very different ways: first, by the value network *v*_{θ}(*s*_{L}); and second, by the outcome *z*_{L} of a random rollout played out until terminal step *T* using the fast rollout policy *p*_{π}; these evaluations are combined, using a mixing parameter *λ*, into a leaf evaluation *V*(*s*_{L})

At the end of simulation, the action values and visit counts of all traversed edges are updated. Each edge accumulates the visit count and mean evaluation of all simulations passing through that edge

where is the leaf node from the *i*th simulation, and 1(*s*, *a*, *i*) indicates whether an edge (*s*, *a*) was traversed during the *i*th simulation. Once the search is complete, the algorithm chooses the most visited move from the root position.

It is worth noting that the SL policy network *p*_{σ} performed better in AlphaGo than the stronger RL policy network *p*_{ρ}, presumably because humans select a diverse beam of promising moves, whereas RL optimizes for the single best move. However, the value function derived from the stronger RL policy network performed better in AlphaGo than a value function derived from the SL policy network.

Evaluating policy and value networks requires several orders of magnitude more computation than traditional search heuristics. To efficiently combine MCTS with deep neural networks, AlphaGo uses an asynchronous multi-threaded search that executes simulations on CPUs, and computes policy and value networks in parallel on GPUs. The final version of AlphaGo used 40 search threads, 48 CPUs, and 8 GPUs. We also implemented a distributed version of AlphaGo that exploited multiple machines, 40 search threads, 1,202 CPUs and 176 GPUs. The Methods section provides full details of asynchronous and distributed MCTS.

## Evaluating the playing strength of AlphaGo

To evaluate AlphaGo, we ran an internal tournament among variants of AlphaGo and several other Go programs, including the strongest commercial programs Crazy Stone^{13} and Zen, and the strongest open source programs Pachi^{14} and Fuego^{15}. All of these programs are based on high-performance MCTS algorithms. In addition, we included the open source program GnuGo, a Go program using state-of-the-art search methods that preceded MCTS. All programs were allowed 5 s of computation time per move.

The results of the tournament (see Fig. 4a) suggest that single-machine AlphaGo is many *dan* ranks stronger than any previous Go program, winning 494 out of 495 games (99.8%) against other Go programs. To provide a greater challenge to AlphaGo, we also played games with four handicap stones (that is, free moves for the opponent); AlphaGo won 77%, 86%, and 99% of handicap games against Crazy Stone, Zen and Pachi, respectively. The distributed version of AlphaGo was significantly stronger, winning 77% of games against single-machine AlphaGo and 100% of its games against other programs.

We also assessed variants of AlphaGo that evaluated positions using just the value network (*λ* = 0) or just rollouts (*λ* = 1) (see Fig. 4b). Even without rollouts AlphaGo exceeded the performance of all other Go programs, demonstrating that value networks provide a viable alternative to Monte Carlo evaluation in Go. However, the mixed evaluation (*λ* = 0.5) performed best, winning ≥95% of games against other variants. This suggests that the two position-evaluation mechanisms are complementary: the value network approximates the outcome of games played by the strong but impractically slow *p*_{ρ}, while the rollouts can precisely score and evaluate the outcome of games played by the weaker but faster rollout policy *p*_{π}. Figure 5 visualizes the evaluation of a real game position by AlphaGo.

Finally, we evaluated the distributed version of AlphaGo against Fan Hui, a professional 2 *dan*, and the winner of the 2013, 2014 and 2015 European Go championships. Over 5–9 October 2015 AlphaGo and Fan Hui competed in a formal five-game match. AlphaGo won the match 5 games to 0 (Fig. 6 and Extended Data Table 1). This is the first time that a computer Go program has defeated a human professional player, without handicap, in the full game of Go—a feat that was previously believed to be at least a decade away^{3,7,31}.

## Discussion

In this work we have developed a Go program, based on a combination of deep neural networks and tree search, that plays at the level of the strongest human players, thereby achieving one of artificial intelligence’s “grand challenges”^{31,32,33}. We have developed, for the first time, effective move selection and position evaluation functions for Go, based on deep neural networks that are trained by a novel combination of supervised and reinforcement learning. We have introduced a new search algorithm that successfully combines neural network evaluations with Monte Carlo rollouts. Our program AlphaGo integrates these components together, at scale, in a high-performance tree search engine.

During the match against Fan Hui, AlphaGo evaluated thousands of times fewer positions than Deep Blue did in its chess match against Kasparov^{4}; compensating by selecting those positions more intelligently, using the policy network, and evaluating them more precisely, using the value network—an approach that is perhaps closer to how humans play. Furthermore, while Deep Blue relied on a handcrafted evaluation function, the neural networks of AlphaGo are trained directly from gameplay purely through general-purpose supervised and reinforcement learning methods.

Go is exemplary in many ways of the difficulties faced by artificial intelligence^{33,34}: a challenging decision-making task, an intractable search space, and an optimal solution so complex it appears infeasible to directly approximate using a policy or value function. The previous major breakthrough in computer Go, the introduction of MCTS, led to corresponding advances in many other domains; for example, general game-playing, classical planning, partially observed planning, scheduling, and constraint satisfaction^{35,36}. By combining tree search with policy and value networks, AlphaGo has finally reached a professional level in Go, providing hope that human-level performance can now be achieved in other seemingly intractable artificial intelligence domains.

## Methods

### Online Methods

#### Problem setting

Many games of perfect information, such as chess, checkers, othello, backgammon and Go, may be defined as alternating Markov games^{39}. In these games, there is a state space (where state includes an indication of the current player to play); an action space defining the legal actions in any given state *s* ∈ ; a state transition function *f*(*s*, *a*, *ξ*) defining the successor state after selecting action *a* in state *s* and random input *ξ* (for example, dice); and finally a reward function *r*^{i}(*s*) describing the reward received by player *i* in state *s*. We restrict our attention to two-player zero-sum games, *r*^{1}(*s*) = −*r*^{2}(*s*) = *r*(*s*), with deterministic state transitions, *f*(*s*, *a*, *ξ*) = *f*(*s*, *a*), and zero rewards except at a terminal time step *T*. The outcome of the game *z*_{t} = ±*r*(*s*_{T}) is the terminal reward at the end of the game from the perspective of the current player at time step *t*. A policy *p*(*a*|*s*) is a probability distribution over legal actions . A value function is the expected outcome if all actions for both players are selected according to policy *p*, that is, . Zero-sum games have a unique optimal value function *v**(*s*) that determines the outcome from state *s* following perfect play by both players,

#### Prior work

The optimal value function can be computed recursively by minimax (or equivalently negamax) search^{40}. Most games are too large for exhaustive minimax tree search; instead, the game is truncated by using an approximate value function *v*(*s*) ≈ *v*^{*}(*s*) in place of terminal rewards. Depth-first minimax search with alpha–beta pruning^{40} has achieved superhuman performance in chess^{4}, checkers^{5} and othello^{6}, but it has not been effective in Go^{7}.

Reinforcement learning can learn to approximate the optimal value function directly from games of self-play^{39}. The majority of prior work has focused on a linear combination *v*_{θ}(*s*) = *φ*(*s*) · *θ* of features *φ*(*s*) with weights *θ*. Weights were trained using temporal-difference learning^{41} in chess^{42,43}, checkers^{44,45} and Go^{30}; or using linear regression in othello^{6} and Scrabble^{9}. Temporal-difference learning has also been used to train a neural network to approximate the optimal value function, achieving superhuman performance in backgammon^{46}; and achieving weak *kyu*-level performance in small-board Go^{28,29,47} using convolutional networks.

An alternative approach to minimax search is Monte Carlo tree search (MCTS)^{11,12}, which estimates the optimal value of interior nodes by a double approximation, . The first approximation, , uses *n* Monte Carlo simulations to estimate the value function of a simulation policy *P*^{n}. The second approximation, , uses a simulation policy *P*^{n} in place of minimax optimal actions. The simulation policy selects actions according to a search control function , such as UCT^{12}, that selects children with higher action values, *Q*^{n}(*s*, *a*) = −*V*^{n}(*f*(*s*, *a*)), plus a bonus *u*(*s*, *a*) that encourages exploration; or in the absence of a search tree at state *s*, it samples actions from a fast rollout policy . As more simulations are executed and the search tree grows deeper, the simulation policy becomes informed by increasingly accurate statistics. In the limit, both approximations become exact and MCTS (for example, with UCT) converges^{12} to the optimal value function . The strongest current Go programs are based on MCTS^{13,14,15,36}.

MCTS has previously been combined with a policy that is used to narrow the beam of the search tree to high-probability moves^{13}; or to bias the bonus term towards high-probability moves^{48}. MCTS has also been combined with a value function that is used to initialize action values in newly expanded nodes^{16}, or to mix Monte Carlo evaluation with minimax evaluation^{49}. By contrast, AlphaGo’s use of value functions is based on truncated Monte Carlo search algorithms^{8,9}, which terminate rollouts before the end of the game and use a value function in place of the terminal reward. AlphaGo’s position evaluation mixes full rollouts with truncated rollouts, resembling in some respects the well-known temporal-difference learning algorithm TD(*λ*). AlphaGo also differs from prior work by using slower but more powerful representations of the policy and value function; evaluating deep neural networks is several orders of magnitude slower than linear representations and must therefore occur asynchronously.

The performance of MCTS is to a large degree determined by the quality of the rollout policy. Prior work has focused on handcrafted patterns^{50} or learning rollout policies by supervised learning^{13}, reinforcement learning^{16}, simulation balancing^{51,52} or online adaptation^{30,53}; however, it is known that rollout-based position evaluation is frequently inaccurate^{54}. AlphaGo uses relatively simple rollouts, and instead addresses the challenging problem of position evaluation more directly using value networks.

#### Search algorithm

To efficiently integrate large neural networks into AlphaGo, we implemented an asynchronous policy and value MCTS algorithm (APV-MCTS). Each node *s* in the search tree contains edges (*s*, *a*) for all legal actions . Each edge stores a set of statistics,

where *P*(*s*, *a*) is the prior probability, *W*_{v}(*s*, *a*) and *W*_{r}(*s*, *a*) are Monte Carlo estimates of total action value, accumulated over *N*_{v}(*s*, *a*) and *N*_{r}(*s*, *a*) leaf evaluations and rollout rewards, respectively, and *Q*(*s*, *a*) is the combined mean action value for that edge. Multiple simulations are executed in parallel on separate search threads. The APV-MCTS algorithm proceeds in the four stages outlined in Fig. 3.

*Selection (Fig. 3a)*. The first in-tree phase of each simulation begins at the root of the search tree and finishes when the simulation reaches a leaf node at time step *L*. At each of these time steps, *t* < *L*, an action is selected according to the statistics in the search tree, using a variant of the PUCT algorithm^{48}, , where *c*_{puct} is a constant determining the level of exploration; this search control strategy initially prefers actions with high prior probability and low visit count, but asymptotically prefers actions with high action value.

*Evaluation (Fig. 3c)*. The leaf position *s*_{L} is added to a queue for evaluation *v*_{θ}(*s*_{L}) by the value network, unless it has previously been evaluated. The second rollout phase of each simulation begins at leaf node *s*_{L} and continues until the end of the game. At each of these time-steps, *t* ≥ *L*, actions are selected by both players according to the rollout policy, . When the game reaches a terminal state, the outcome is computed from the final score.

*Backup (Fig. 3d)*. At each in-tree step *t* ≤ *L* of the simulation, the rollout statistics are updated as if it has lost *n*_{vl} games, *N*_{r}(*s*_{t}, *a*_{t}) ← *N*_{r}(*s*_{t}, *a*_{t}) + *n*_{vl}; *W*_{r}(*s*_{t}, *a*_{t}) ← *W*_{r}(*s*_{t}, *a*_{t}) −*n*_{vl}; this virtual loss^{55} discourages other threads from simultaneously exploring the identical variation. At the end of the simulation, t he rollout statistics are updated in a backward pass through each step *t* ≤ *L*, replacing the virtual losses by the outcome, *N*_{r}(*s*_{t}, *a*_{t}) ← *N*_{r}(*s*_{t}, *a*_{t}) −*n*_{vl} + 1; *W*_{r}(*s*_{t}, *a*_{t}) ← *W*_{r}(*s*_{t}, *a*_{t}) + *n*_{vl} + *z*_{t}. Asynchronously, a separate backward pass is initiated when the evaluation of the leaf position *s*_{L} completes. The output of the value network *v*_{θ}(*s*_{L}) is used to update value statistics in a second backward pass through each step *t* ≤ *L*, *N*_{v}(*s*_{t}, *a*_{t}) ← *N*_{v}(*s*_{t}, *a*_{t}) + 1, *W*_{v}(*s*_{t}, *a*_{t}) ← *W*_{v}(*s*_{t}, *a*_{t}) + *v*_{θ}(*s*_{L}). The overall evaluation of each state action is a weighted average of the Monte Carlo estimates, , that mixes together the value network and rollout evaluations with weighting parameter *λ*. All updates are performed lock-free^{56}.

*Expansion (Fig. 3b)*. When the visit count exceeds a threshold, *N*_{r}(*s*, *a*) > *n*_{thr}, the successor state *s*′ = *f*(*s*, *a*) is added to the search tree. The new node is initialized to {*N*_{}(*s*′, *a*) =_{ }*N*_{r}(*s*′, *a*) = 0, *W*_{}(*s*′, *a*) =_{ }*W*_{r}(*s*′, *a*) = 0, *P*(*s*′,*a*) = *p*_{σ}(*a*|*s*′)}, using a tree policy *p*_{}_{τ}(*a*|*s*′) (similar to the rollout policy but with more features, see Extended Data Table 4) to provide placeholder prior probabilities for action selection. The position *s*′ is also inserted into a queue for asynchronous GPU evaluation by the policy network. Prior probabilities are computed by the SL policy network with a softmax temperature set to *β*; these replace the placeholder prior probabilities, , using an atomic update. The threshold *n*_{thr} is adjusted dynamically to ensure that the rate at which positions are added to the policy queue matches the rate at which the GPUs evaluate the policy network. Positions are evaluated by both the policy network and the value network using a mini-batch size of 1 to minimize end-to-end evaluation time.

We also implemented a distributed APV-MCTS algorithm. This architecture consists of a single master machine that executes the main search, many remote worker CPUs that execute asynchronous rollouts, and many remote worker GPUs that execute asynchronous policy and value network evaluations. The entire search tree is stored on the master, which only executes the in-tree phase of each simulation. The leaf positions are communicated to the worker CPUs, which execute the rollout phase of simulation, and to the worker GPUs, which compute network features and evaluate the policy and value networks. The prior probabilities of the policy network are returned to the master, where they replace placeholder prior probabilities at the newly expanded node. The rewards from rollouts and the value network outputs are each returned to the master, and backed up the originating search path.

At the end of search AlphaGo selects the action with maximum visit count; this is less sensitive to outliers than maximizing action value^{15}. The search tree is reused at subsequent time steps: the child node corresponding to the played action becomes the new root node; the subtree below this child is retained along with all its statistics, while the remainder of the tree is discarded. The match version of AlphaGo continues searching during the opponent’s move. It extends the search if the action maximizing visit count and the action maximizing action value disagree. Time controls were otherwise shaped to use most time in the middle-game^{57}. AlphaGo resigns when its overall evaluation drops below an estimated 10% probability of winning the game, that is, .

AlphaGo does not employ the all-moves-as-first^{10} or rapid action value estimation^{58} heuristics used in the majority of Monte Carlo Go programs; when using policy networks as prior knowledge, these biased heuristics do not appear to give any additional benefit. In addition AlphaGo does not use progressive widening^{13}, dynamic *komi*^{59} or an opening book^{60}. The parameters used by AlphaGo in the Fan Hui match are listed in Extended Data Table 5.

#### Rollout policy

The rollout policy is a linear softmax policy based on fast, incrementally computed, local pattern-based features consisting of both ‘response’ patterns around the previous move that led to state *s*, and ‘non-response’ patterns around the candidate move *a* in state *s*. Each non-response pattern is a binary feature matching a specific 3 × 3 pattern centred on *a*, defined by the colour (black, white, empty) and liberty count (1, 2, ≥3) for each adjacent intersection. Each response pattern is a binary feature matching the colour and liberty count in a 12-point diamond-shaped pattern^{21} centred around the previous move. Additionally, a small number of handcrafted local features encode common-sense Go rules (see Extended Data Table 4). Similar to the policy network, the weights *π* of the rollout policy are trained from 8 million positions from human games on the Tygem server to maximize log likelihood by stochastic gradient descent. Rollouts execute at approximately 1,000 simulations per second per CPU thread on an empty board.

Our rollout policy *p*_{π}(*a*|*s*) contains less handcrafted knowledge than state-of-the-art Go programs^{13}. Instead, we exploit the higher-quality action selection within MCTS, which is informed both by the search tree and the policy network. We introduce a new technique that caches all moves from the search tree and then plays similar moves during rollouts; a generalization of the ‘last good reply’ heuristic^{53}. At every step of the tree traversal, the most probable action is inserted into a hash table, along with the 3 × 3 pattern context (colour, liberty and stone counts) around both the previous move and the current move. At each step of the rollout, the pattern context is matched against the hash table; if a match is found then the stored move is played with high probability.

#### Symmetries

In previous work, the symmetries of Go have been exploited by using rotationally and reflectionally invariant filters in the convolutional layers^{24,28,29}. Although this may be effective in small neural networks, it actually hurts performance in larger networks, as it prevents the intermediate filters from identifying specific asymmetric patterns^{23}. Instead, we exploit symmetries at run-time by dynamically transforming each position *s* using the dihedral group of eight reflections and rotations, *d*_{1}(*s*), …, *d*_{8}(*s*). In an explicit symmetry ensemble, a mini-batch of all 8 positions is passed into the policy network or value network and computed in parallel. For the value network, the output values are simply averaged, . For the policy network, the planes of output probabilities are rotated/reflected back into the original orientation, and averaged together to provide an ensemble prediction, ; this approach was used in our raw network evaluation (see Extended Data Table 3). Instead, APV-MCTS makes use of an implicit symmetry ensemble that randomly selects a single rotation/reflection *j* ∈ [1, 8] for each evaluation. We compute exactly one evaluation for that orientation only; in each simulation we compute the value of leaf node *s*_{L} by *v*_{θ}(*d*_{j}(*s*_{L})), and allow the search procedure to average over these evaluations. Similarly, we compute the policy network for a single, randomly selected rotation/reflection, .

#### Policy network: classification

We trained the policy network *p*_{σ} to classify positions according to expert moves played in the KGS data set. This data set contains 29.4 million positions from 160,000 games played by KGS 6 to 9 *dan* human players; 35.4% of the games are handicap games. The data set was split into a test set (the first million positions) and a training set (the remaining 28.4 million positions). Pass moves were excluded from the data set. Each position consisted of a raw board description *s* and the move *a* selected by the human. We augmented the data set to include all eight reflections and rotations of each position. Symmetry augmentation and input features were pre-computed for each position. For each training step, we sampled a randomly selected mini-batch of *m* samples from the augmented KGS data set, and applied an asynchronous stochastic gradient descent update to maximize the log likelihood of the action, . The step size *α* was initialized to 0.003 and was halved every 80 million training steps, without momentum terms, and a mini-batch size of *m* = 16. Updates were applied asynchronously on 50 GPUs using DistBelief ^{61}; gradients older than 100 steps were discarded. Training took around 3 weeks for 340 million training steps.

#### Policy network: reinforcement learning

We further trained the policy network by policy gradient reinforcement learning^{25,26}. Each iteration consisted of a mini-batch of *n* games played in parallel, between the current policy network *p*_{ρ} that is being trained, and an opponent that uses parameters *ρ*^{−} from a previous iteration, randomly sampled from a pool of opponents, so as to increase the stability of training. Weights were initialized to *ρ* = *ρ*^{−} = *σ*. Every 500 iterations, we added the current parameters *ρ* to the opponent pool. Each game *i* in the mini-batch was played out until termination at step *T*^{i}, and then scored to determine the outcome from each player’s perspective. The games were then replayed to determine the policy gradient update, , using the REINFORCE algorithm^{25} with baseline for variance reduction. On the first pass through the training pipeline, the baseline was set to zero; on the second pass we used the value network *v*_{θ}(*s*) as a baseline; this provided a small performance boost. The policy network was trained in this way for 10,000 mini-batches of 128 games, using 50 GPUs, for one day.

#### Value network: regression

We trained a value network to approximate the value function of the RL policy network *p*_{ρ}. To avoid overfitting to the strongly correlated positions within games, we constructed a new data set of uncorrelated self-play positions. This data set consisted of over 30 million positions, each drawn from a unique game of self-play. Each game was generated in three phases by randomly sampling a time step *U* ~ unif{1, 450}, and sampling the first *t* = 1,… *U* − 1 moves from the SL policy network, *a*_{t} ~ *p*_{σ}(·|*s*_{t}); then sampling one move uniformly at random from available moves, *a*_{U} ~ unif{1, 361} (repeatedly until *a*_{U} is legal); then sampling the remaining sequence of moves until the game terminates, *t* = *U* + 1, … *T*, from the RL policy network, *a*_{t} ~ *p*_{ρ}(·|*s*_{t}). Finally, the game is scored to determine the outcome *z*_{t} = ±*r*(*s*_{T}). Only a single training example (*s*_{U+1}, *z*_{U+1}) is added to the data set from each game. This data provides unbiased samples of the value function . During the first two phases of generation we sample from noisier distributions so as to increase the diversity of the data set. The training method was identical to SL policy network training, except that the parameter update was based on mean squared error between the predicted values and the observed rewards, . The value network was trained for 50 million mini-batches of 32 positions, using 50 GPUs, for one week.

#### Features for policy/value network

Each position *s* was pre-processed into a set of 19 × 19 feature planes. The features that we use come directly from the raw representation of the game rules, indicating the status of each intersection of the Go board: stone colour, liberties (adjacent empty points of stone’s chain), captures, legality, turns since stone was played, and (for the value network only) the current colour to play. In addition, we use one simple tactical feature that computes the outcome of a ladder search^{7}. All features were computed relative to the current colour to play; for example, the stone colour at each intersection was represented as either player or opponent rather than black or white. Each integer feature value is split into multiple 19 × 19 planes of binary values (one-hot encoding). For example, separate binary feature planes are used to represent whether an intersection has 1 liberty, 2 liberties,…, ≥8 liberties. The full set of feature planes are listed in Extended Data Table 2.

#### Neural network architecture

The input to the policy network is a 19 × 19 × 48 image stack consisting of 48 feature planes. The first hidden layer zero pads the input into a 23 × 23 image, then convolves *k* filters of kernel size 5 × 5 with stride 1 with the input image and applies a rectifier nonlinearity. Each of the subsequent hidden layers 2 to 12 zero pads the respective previous hidden layer into a 21 × 21 image, then convolves *k* filters of kernel size 3 × 3 with stride 1, again followed by a rectifier nonlinearity. The final layer convolves 1 filter of kernel size 1 × 1 with stride 1, with a different bias for each position, and applies a softmax function. The match version of AlphaGo used *k* = 192 filters; Fig. 2b and Extended Data Table 3 additionally show the results of training with *k* = 128, 256 and 384 filters.

The input to the value network is also a 19 × 19 × 48 image stack, with an additional binary feature plane describing the current colour to play. Hidden layers 2 to 11 are identical to the policy network, hidden layer 12 is an additional convolution layer, hidden layer 13 convolves 1 filter of kernel size 1 × 1 with stride 1, and hidden layer 14 is a fully connected linear layer with 256 rectifier units. The output layer is a fully connected linear layer with a single tanh unit.

#### Evaluation

We evaluated the relative strength of computer Go programs by running an internal tournament and measuring the Elo rating of each program. We estimate the probability that program *a* will beat program *b* by a logistic function , and estimate the ratings *e*(·) by Bayesian logistic regression, computed by the *BayesElo* program^{37} using the standard constant *c*_{elo} = 1/400. The scale was anchored to the *BayesElo* rating of professional Go player Fan Hui (2,908 at date of submission)^{62}. All programs received a maximum of 5 s computation time per move; games were scored using Chinese rules with a *komi* of 7.5 points (extra points to compensate white for playing second). We also played handicap games where AlphaGo played white against existing Go programs; for these games we used a non-standard handicap system in which *komi* was retained but black was given additional stones on the usual handicap points. Using these rules, a handicap of *K* stones is equivalent to giving *K* − 1 free moves to black, rather than *K* − 1/2 free moves using standard no-*komi* handicap rules. We used these handicap rules because AlphaGo’s value network was trained specifically to use a *komi* of 7.5.

With the exception of distributed AlphaGo, each computer Go program was executed on its own single machine, with identical specifications, using the latest available version and the best hardware configuration supported by that program (see Extended Data Table 6). In Fig. 4, approximate ranks of computer programs are based on the highest KGS rank achieved by that program; however, the KGS version may differ from the publicly available version.

The match against Fan Hui was arbitrated by an impartial referee. Five formal games and five informal games were played with 7.5 *komi*, no handicap, and Chinese rules. AlphaGo won these games 5–0 and 3–2 respectively (Fig. 6 and Extended Data Table 1). Time controls for formal games were 1 h main time plus three periods of 30 s *byoyomi*. Time controls for informal games were three periods of 30 s *byoyomi*. Time controls and playing conditions were chosen by Fan Hui in advance of the match; it was also agreed that the overall match outcome would be determined solely by the formal games. To approximately assess the relative rating of Fan Hui to computer Go programs, we appended the results of all ten games to our internal tournament results, ignoring differences in time controls.

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## Acknowledgements

We thank Fan Hui for agreeing to play against AlphaGo; T. Manning for refereeing the match; R. Munos and T. Schaul for helpful discussions and advice; A. Cain and M. Cant for work on the visuals; P. Dayan, G. Wayne, D. Kumaran, D. Purves, H. van Hasselt, A. Barreto and G. Ostrovski for reviewing the paper; and the rest of the DeepMind team for their support, ideas and encouragement.

## Author information

## Author notes

- David Silver
- & Aja Huang

These authors contributed equally to this work.

## Affiliations

### Google DeepMind, 5 New Street Square, London EC4A 3TW, UK

- David Silver
- , Aja Huang
- , Chris J. Maddison
- , Arthur Guez
- , Laurent Sifre
- , George van den Driessche
- , Julian Schrittwieser
- , Ioannis Antonoglou
- , Veda Panneershelvam
- , Marc Lanctot
- , Sander Dieleman
- , Dominik Grewe
- , Nal Kalchbrenner
- , Timothy Lillicrap
- , Madeleine Leach
- , Koray Kavukcuoglu
- , Thore Graepel
- & Demis Hassabis

### Google, 1600 Amphitheatre Parkway, Mountain View, California 94043, USA

- John Nham
- & Ilya Sutskever

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### Contributions

A.H., G.v.d.D., J.S., I.A., M.La., A.G., T.G. and D.S. designed and implemented the search in AlphaGo. C.J.M., A.G., L.S., A.H., I.A., V.P., S.D., D.G., N.K., I.S., K.K. and D.S. designed and trained the neural networks in AlphaGo. J.S., J.N., A.H. and D.S. designed and implemented the evaluation framework for AlphaGo. D.S., M.Le., T.L., T.G., K.K. and D.H. managed and advised on the project. D.S., T.G., A.G. and D.H. wrote the paper.

### Competing interests

The authors declare no competing financial interests.

## Corresponding authors

Correspondence to David Silver or Demis Hassabis.

## Extended data

## Extended data tables

- 1.
### Details of match between AlphaGo and Fan Hui

- 2.
### Input features for neural networks

- 3.
### Supervised learning results for the policy network

- 4.
### Input features for rollout and tree policy

- 5.
### Parameters used by AlphaGo

- 6.
### Results of a tournament between different Go programs

- 7.
### Results of a tournament between different variants of AlphaGo

- 8.
### Results of a tournament between AlphaGo and distributed AlphaGo, testing scalability with hardware

- 9.
### Cross-table of win rates in per cent between programs

- 10.
### Cross-table of win rates in per cent between programs in the single-machine scalability study

- 11.
### Cross-table of win rates in per cent between programs in the distributed scalability study

## Supplementary information

## Zip files

- 1.
### Supplementary Information

This zipped file contains game records for the 5 formal match games played between AlphaGo and Fan Hui.