Games with large branching factors pose a significant challenge for game tree search algorithms. In this paper, we address this problem with a sampling strategy for Monte Carlo Tree Search (MCTS) algorithms called na " i ve sampling , based on a variant of the Multi-armed Bandit problem called Combinatorial Multi-armed Bandits (CMAB). We analyze the theoretical properties of several variants of na " i ve sampling , and empirically compare it against the other existing strategies in the literature for CMABs. We then evaluate these strategies in the context of real-time strategy (RTS) games, a genre of computer games characterized by their very large branching factors. Our results show that as the branching factor grows, na " i ve sampling outperforms the other sampling strategies.
 We present CLASSQ-L (for: class Q-learning) an application of the Q-learning reinforcement learning algorithm to play complete Wargus games. Wargus is a real-time strategy game where players control armies consisting of units of different classes (e.g., archers, knights). CLASSQ-L uses a single table for each class of unit so that each unit is controlled and updates its class’ Q-table. This enables rapid learning as in Wargus there are many units of the same class. We present initial results of CLASSQ-L against a variety of opponents. This paper presents an evaluation of the suitability of reinforcement learning (RL) algorithms to perform the task of micro-managing combat units in the commercial real-time strategy (RTS) game StarCraft:Broodwar (SC:BW). The applied techniques are variations of the common Q-learning and Sarsa algorithms, both simple one-step versions as well as more sophisticated versions that use eligibility traces to offset the problem of delayed reward. The aim is the design of an agent that is able to learn in an unsupervised manner in a complex environment, eventually taking over tasks that had previously been performed by non-adaptive, deterministic game AI. The preliminary results presented in this paper show the viability of the RL algorithms at learning the selected task. Depending on whether the focus lies on maximizing the reward or on the speed of learning, among the evaluated algorithms one-step Q-learning and Sarsa(λ) prove best at learning to manage combat units. The goal of transfer learning is to use the knowledge acquired in a set of source tasks to improve performance in a related but previously unseen target task. In this paper, we present a multilayered architecture named CAse-Based Reinforcement Learner (CARL). It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to achieve transfer while playing against the Game AI across a variety of scenarios in MadRTSTM, a commercial Real Time Strategy game. Our experiments demonstrate that CARL not only performs well on individual tasks but also exhibits significant performance gains when allowed to transfer knowledge from previous tasks. We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms. We propose micromanagement tasks, which present the problem of the short-term, low-level control of army members during a battle. From a reinforcement learning point of view, these scenarios are challenging because the state-action space is very large, and because there is no obvious feature representation for the state-action evaluation function. We describe our approach to tackle the micromanagement scenarios with deep neural network controllers from raw state features given by the game engine. In addition, we present a heuristic reinforcement learning algorithm which combines direct exploration in the policy space and backpropagation. This algorithm allows for the collection of traces for learning using deterministic policies, which appears much more efficient than, for example, -greedy exploration. Experiments show that with this algorithm, we successfully learn non-trivial strategies for scenarios with armies of up to 15 agents, where both Q-learning and REINFORCE struggle.