Wired has a nice article about the two most brilliant moves in the historic match between AlphaGo and Lee Sedol.

http://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/

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Wired has a nice article about the two most brilliant moves in the historic match between AlphaGo and Lee Sedol.

http://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/

A partially observable Markov decision process (POMDP) can be informally defined as a world in which an agent can take actions and gain rewards. The world has a set of possible world states ** S**. This set can be finite (e.g. mine sweeper) or infinite (e. g. a robotic car in a parking lot). The world is only partially observable, so if we try to program an agent to act in this world, the robot does not know the state of the entire world, rather it only gets observations that partially reveal the state of the world. The set of possible observations is typically called

The Daily Mail reports that the Computer Poker Research Group at the University of Alberta seems to have solved heads-up limit hold’em poker.

You can play against their AI online.

(My thanks to Glen for emailing me the story!)

Mnih, Kavukcuoglu, Silver, Graves, Antonoglon, Wierstra, and Riedmiller authored the paper “Playing Atari with Deep Reinforcement Learning” which describes and an Atari game playing program created by the company Deep Mind (recently acquired by Google). The AI did not just learn how to pay one game. It learned to play seven Atari games without game specific direction from the programmers. (The same learning parameters, neural network topologies, and algorithms were used for every game).

The 2600 Atari gaming system was quite popular in the late 1970’s and the early 1980’s. The games ran with only four kilobytes of RAM and a 210 x 160 pixel display with 128 colors. Various machine learning techniques have been applied to the old Atari games using the Arcade Learning Environment which precisely reproduces the Atari 2600 gaming system. (See e.g. “An Object-Oriented Representation for Efficient Reinforcement Learning” by Diuk, Cohen, and Littman 2008, ”HyperNEAT-GGP:A HyperNEAT-based Atari General Game Player” by Hausknecht, Khandelwal, Miikkulainen, and Stone 2012, “Application of TEXPLORE on Atari Games “ by Shung Zhang , ”A Neuroevolution Approach to General Atari Game Playing” by Hausknect, Lehman,” Miikkulalianen, and Stone 2014, and “Replicating the Paper ‘Playing Atari with Deep Reinforcement Learning’ ” by Korjus, Kuzovkin, Tampuu, and Pungas 2014.)

Various papers have been written on how computers can learn to pay the Atari games, but most of them used the abstract representations of objects on the screen within the emulator. The Mnih et al AI learned to play the games using only the raw 210 x 160 video and the score. It seems to be the first successful attempt to learn arcade gaming from raw video.

To learn from raw video, they first converted the video to grayscale and then downsampled/cropped to 84 x 84 images. The last four frames were used to determine actions. The 28224 input pixels were run through two hidden convolution neural net layers and one fully connected (no convolution) 256 node hidden layer with a single output for each possible action. Training was done with stochastic gradient decent using random samples drawn from a historical database of previous games played by the AI to improve convergence (This technique known as “experience replay” is described in “Reinforcement learning for robots using neural nets” Long-Ji Lin 1993.)

The objective function for supervised learning is usually a loss function representing the difference between the predicted label and the actual label. For these games the correct action is unknown, so reinforcement learning is used instead of supervised learning. The authors used a variant of Q-learning to train the weights in their neural network. They describe their algorithm in detail and compare it to several historical reinforcement algorithms, so this section of the paper can be used as a brief introduction to reinforcement learning.

The AI was trained to play seven games: Beam Rider, Breakout, Enduro, Pong, Q*bert, Seaquest, and Space Invaders. In six of the seven games, this general game learning algorithm outperformed all previously known reinforcement learning algorithms tested on those games and “surpasses a human expert on three” of the seven games.

So I played the game 2048 and thought it would be fun to create an AI for the game. I wrote my code in Mathematica because it is a fun, terse language with easy access to numerical libraries since I was thinking some simple linear or logit regression ought to give me some easy to code strategies.

The game itself is pretty easy to code in Mathematica. The game is played on a 4×4 grid of tiles. Each tile in the grid can contain either a blank (I will hereafter use a _ to denote a blank), or one of the numbers 2,4,8,16,32,64,128, 256, 512, 1024, or 2048. On every turn, you can shift the board right, left, up, or down. When you shift the board, any adjacent matching numbers will combine.

The object of the game is to get one 2048 tile which usually requires around a thousand turns.

Here’s an example. Suppose you have the board below.

If you shift right, the top row will become _, _, 4, 16, because the two twos will combine. Nothing else will combine.

Shifting left gives the same result as shifting right except all of the tiles are on the left side instead of the right.

If we look back at the original image (below), we can see that there are two sixteen tiles stacked vertically.

So the original board (above) shifted down combines the two sixteens into a thirty-two (below) setting some possible follow-up moves like combining the twos on the bottom row or combining the two thirty-twos into a sixty-four.

There are a few other game features that make things more difficult. After every move, if the board changes, then a two or four tile randomly fills an empty space. So, the sum of the tiles increases by two or four every move.

The first strategy I tried was random play. A typical random play board will look something like this after 50 moves.

If you play for a while, you will think that this position is in a little difficult because the high numbers are not clumped, so they might be hard to combine. A typical random game ends in a position like this one.

In the board above, the random player is in deep trouble. His large tiles are near the center, the 128 is not beside the 64, there are two 32 tiles separated by the 64, and the board is almost filled. The player can only move up or down. In this game the random player incorrectly moved up combining the two twos in the lower right and the new two tile appeared in the lower right corner square ending the game.

I ran a 1000 random games and found that the random player ends the game with an average tile total of 252. Here is a histogram of the resulting tile totals at the end of all 1000 games.

(The random player’s tile total was between 200 and 220 one hundred and twenty seven times. He was between 400 and 420 thirteen times.)

The random player’s largest tile was 256 5% of the time, 128 44% of the time, 64 42% of the time, 32 8% of the time, and 16 0.5% of the time. It seems almost impossible to have a max of 16 at the end. Here is how the random player achieved this ignominious result.

So what’s a simple improvement? About the simplest possible improvement is to try to score the most points every turn—the greedy strategy. You get the most points by making the largest tile possible. In the board below, the player has the options of going left or right combining the 32 tiles, or moving up or down combining the two 2 tiles in the lower left.

The greedy strategy would randomly choose between left or right combining the two 32 tiles. The greedy action avoids splitting the 32 tiles.

I ran 1000 greedy player games games. The average tile total at the end of a greedy player simulation was 399—almost a 60% improvement over random play. Here is a histogram of the greedy player’s results.

If you look closely, you will see that is a tiny bump just above 1000. In all 1000 games, the greedy player did manage to get a total above 1000 once, but never got the elusive 1024 tile. He got the 512 tile 2% of the time, the 256 tile 43% of the time, the 128 44%, the 64 11%, and in three games, his maximum tile was 32.

I ran a number of other simple strategies before moving onto the complex look ahead strategies like UCT. More on that in my next post.

In “Generalized Thompson sampling for sequential decision-making and causal inference“, Ortega and Braun (2013) give a short history of Thompson sampling (see [1], [2], [3], [4]) and report on the relationship between intelligent agents, evolutionary game theory, Bayesian inference, KL Divergence, and Thompson sampling. They develop a generalization of Thompson sampling based on a Bayesian prior over a distribution of environments. Some numerical results are provided. Here’s the abstract:

Recently, it has been shown how sampling actions from the predictive distribution over the optimal action—sometimes called Thompson sampling—can be applied to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution can then be constructed by a Bayesian superposition of the optimal policies weighted by their posterior probability that is updated by Bayesian inference and causal calculus. Here we discuss three important features of this approach. First, we discuss in how far such Thompson sampling can be regarded as a natural consequence of the Bayesian modeling of policy uncertainty. Second, we show how Thompson sampling can be used to study interactions between multiple adaptive agents, thus, opening up an avenue of game-theoretic analysis. Third, we show how Thompson sampling can be applied to infer causal relationships when interacting with an environment in a sequential fashion. In summary, our results suggest that Thompson sampling might not merely be a useful heuristic, but a principled method to address problems of adaptive sequential decision-making and causal inference.

In “Efﬁcient Reinforcement Learning for High Dimensional Linear Quadratic Systems” Ibrahimi, Javanmard, and Van Roy (2012) present a fast reinforcement learning solution for linear control systems problems with quadratic costs. The problem is to minimize the total cost $\min_u \sum_{i=1}^T x_i^t Q x_i + u_i^t R u_i$ for the control system $x_{i+1} = A x_i + B u_i + w_i$ where $x\in R^j$ is the state of the system; $u\in R^k$ is the control; $Q, R, A,$ and $B$ are matrices; and $w_i\in R^j$ is a random multivariate normally distributed vector.

NIPS was pretty fantastic this year. There were a number of breakthroughs in the areas that interest me most: Markov Decision Processes, Game Theory, Multi-Armed Bandits, and Deep Belief Networks. Here is the list of papers, workshops, and presentations I found the most interesting or potentially useful:

- Representation, Inference and Learning in Structured Statistical Models
- Stochastic Search and Optimization
- Quantum information and the Brain
- Relax and Randomize : From Value to Algorithms (Great)
- Classification with Deep Invariant Scattering Networks
- Discriminative Learning of Sum-Product Networks
- On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes
- A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes
- Regularized Off-Policy TD-Learning
- Multi-Stage Multi-Task Feature Learning
- Graphical Models via Generalized Linear Models (Great)
- No voodoo here! Learning discrete graphical models via inverse covariance estimation (Great)
- Gradient Weights help Nonparametric Regressors
- Dropout: A simple and effective way to improve neural networks (Great)
- Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions
- A Better Way to Pre-Train Deep Boltzmann Machines
- Bayesian Optimization and Decision Making
- Practical Bayesian Optimization of Machine Learning Algorithms
- Modern Nonparametric Methods in Machine Learning
- Deep Learning and Unsupervised Feature Learning

Unfortunately, when you have 30 full day workshops in a two day period, you miss most of them. I could only attend the three listed above. There were many other great ones.

In “Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity“, Ailon (2011) presents a method for ordering a set based on noisy comparisons of elements. He proves that his adaptive method approximates the NP Hard optimal solution (Alon 2006) to within $(1+\epsilon)$ times the minimum error after $f(\epsilon^{-1}, n)$ comparisons where $f$ is a polynomial. Ailon builds on the work of Kenyon-Mathieu and Schudy (2007) who also developed a polynomial time algorithm for approximate ranking. The Kenyon-Mathieu and Schudy algorithm was not query efficient.

In “Active Learning Literature Survey“, Burr Settles (2010) reviews uncertainty sampling (Lewis and Gale, 1994), margin sampling (Scheffer et al., 2001), entropy sampling, optimal experimental design, query-by-committee (Seung et al., 1992), query-by-boosting, query-by-bagging, expected model change, expected error reduction, expected information gain, variance reduction, and density weighted methods. He then comments on theoretical and empirical performance of these methods, practical considerations, and related areas of machine learning including: semi-supervised learning, reinforcement learning, and compression.