Approximate universal artificial intelligence and self-play learning for games

This thesis is split into two independent parts. The first is an investigation of some practical aspects of Marcus Hutter's Universal Artificial Intelligence theory. The main contributions are to show how a very general agent can be built and analysed using the mathematical tools of this theory. Before the work presented in this thesis, it was an open question as to whether this theory was of any relevance to reinforcement learning practitioners. This work suggests that it is indeed relevant and worthy of future investigation. The second part of this thesis looks at self-play learning in two player, deterministic, adversarial turn-based games. The main contribution is the introduction of a new technique for training the weights of a heuristic evaluation function from data collected by classical game tree search algorithms. This method is shown to outperform previous self-play training routines based on Temporal Difference learning when applied to the game of Chess. In particular, the main highlight was using this technique to construct a Chess program that learnt to play master level Chess by tuning a set of initially random weights from self play games

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence

Expert iteration

In this thesis, we study how reinforcement learning algorithms can tackle classical board games without recourse to human knowledge. Specifically, we develop a framework and algorithms which learn to play the board game Hex starting from random play. We first describe Expert Iteration (ExIt), a novel reinforcement learning framework which extends Modified Policy Iteration. ExIt explicitly decomposes the reinforcement learning problem into two parts: planning and generalisation. A planning algorithm explores possible move sequences starting from a particular position to find good strategies from that position, while a parametric function approximator is trained to predict those plans, generalising to states not yet seen. Subsequently, planning is improved by using the approximated policy to guide search, increasing the strength of new plans. This decomposition allows ExIt to combine the benefits of both planning methods and function approximation methods. We demonstrate the effectiveness of the ExIt paradigm by implementing ExIt with two different planning algorithms. First, we develop a version based on Monte Carlo Tree Search (MCTS), a search algorithm which has been successful both in specific games, such as Go, Hex and Havannah, and in general game playing competitions. We then develop a new planning algorithm, Policy Gradient Search (PGS), which uses a model-free reinforcement learning algorithm for online planning. Unlike MCTS, PGS does not require an explicit search tree. Instead PGS uses function approximation within a single search, allowing it to be applied to problems with larger branching factors. Both MCTS-ExIt and PGS-ExIt defeated MoHex 2.0 - the most recent Hex Olympiad winner to be open sourced - in 9 × 9 Hex. More importantly, whereas MoHex makes use of many Hex-specific improvements and knowledge, all our programs were trained tabula rasa using general reinforcement learning methods. This bodes well for ExIt’s applicability to both other games and real world decision making problems

Value targets in off-policy AlphaZero: A new greedy backup

This article presents and evaluates a family of AlphaZero value targets, subsuming previous variants and introducing AlphaZero with greedy backups (A0GB). Current state-of-the-art algorithms for playing board games use sample-based planning, such as Monte Carlo Tree Search (MCTS), combined with deep neural networks (NN) to approximate the value function. These algorithms, of which AlphaZero is a prominent example, are computationally extremely expensive to train, due to their reliance on many neural network evaluations. This limits their practical performance. We improve the training process of AlphaZero by using more effective training targets for the neural network. We introduce a three-dimensional space to describe a family of training targets, covering the original AlphaZero training target as well as the soft-Z and A0C variants from the literature. We demonstrate that A0GB, using a specific new value target from this family, is able to find the optimal policy in a small tabular domain, whereas the original AlphaZero target fails to do so. In addition, we show that soft-Z, A0C and A0GB achieve better performance and faster training than the original AlphaZero target on two benchmark board games (Connect-Four and Breakthrough). Finally, we juxtapose tabular learning with neural network-based value function approximation in Tic-Tac-Toe, and compare the effects of learning targets therein

Learning to Search in Reinforcement Learning

In this thesis, we investigate the use of search based algorithms with deep neural networks to tackle a wide range of problems ranging from board games to video games and beyond. Drawing inspiration from AlphaGo, the first computer program to achieve superhuman performance in the game of Go, we developed a new algorithm AlphaZero. AlphaZero is a general reinforcement learning algorithm that combines deep neural networks with a Monte Carlo Tree search for planning and learning. Starting completely from scratch, without any prior human knowledge beyond the basic rules of the game, AlphaZero managed to achieve superhuman performance in Go, chess and shogi. Subsequently, building upon the success of AlphaZero, we investigated ways to extend our methods to problems in which the rules are not known or cannot be hand-coded. This line of work led to the development of MuZero, a model-based reinforcement learning agent that builds a deterministic internal model of the world and uses it to construct plans in its imagination. We applied our method to Go, chess, shogi and the classic Atari suite of video-games, achieving superhuman performance. MuZero is the first RL algorithm to master a variety of both canonical challenges for high performance planning and visually complex problems using the same principles. Finally, we describe Stochastic MuZero, a general agent that extends the applicability of MuZero to highly stochastic environments. We show that our method achieves superhuman performance in stochastic domains such as backgammon and the classic game of 2048 while matching the performance of MuZero in deterministic ones like Go