We investigate competitive co-evolution of unit micromanagement in real-time strategy games. Although good long-term macro-strategy and good short-term unit micromanagement both impact real-time strategy games performance, this paper focuses on generating quality micro. Better micro, for example, can help players win skirmishes and battles even when outnumbered. Prior work has shown that we can evolve micro to beat a given opponent. We remove the need for a good opponent to evolve against by using competitive co-evolution to evolve high-quality micro for both sides from scratch. We first co-evolve micro to control a group of ranged units versus a group of melee units. We then move to co-evolve micro for a group of ranged and melee units versus a group of ranged and melee units. Results show that competitive co-evolution produces good quality micro and when combined with the well-known techniques of fitness sharing, shared sampling, and a hall of fame takes less time to produce better quality micro than simple co-evolution. We believe these results indicate the viability of co-evolutionary approaches for generating good unit micro-management.
In real-time strategy games (RTS), the player must reason about high-level strategy and planning while having effective tactics and even individual units micro-management. Enabling an artificial agent to deal with such a task entails breaking down the complexity of this environment. For that, we propose to control units locally in the Bayesian sensory motor robot fashion, with higher level orders integrated as perceptions. As complete inference encompassing global strategy down to individual unit needs is intractable, we embrace incompleteness through a hierarchical model able to deal with uncertainty. We developed and applied our approach on a StarCraft1 AI. This paper presents an overview of the existing work on AI for real-time strategy (RTS) games. Specifically, we focus on the work around the game StarCraft, which has emerged in the past few years as the unified test bed for this research. We describe the specific AI challenges posed by RTS games, and overview the solutions that have been explored to address them. Additionally, we also present a summary of the results of the recent StarCraft AI competitions, describing the architectures used by the participants. Finally, we conclude with a discussion emphasizing which problems in the context of RTS game AI have been solved, and which remain open. 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. Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method — Alpha-Beta search for durative moves — that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable — opening the door for a promising line of research on opponent combat modelling.