Real-time imitation-based adaptation of gaming behaviour in modern computer games

Priesterjahn, Steffen; Weimer, Alexander; Eberling, Markus

doi:10.1145/1389095.1389374

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Later, in [11], [12], the same approach was combined with imitation learning to evolve human-like NPCs. McPartland and Gallagher [13] applied reinforcement learning to solve combat and navigation tasks in a self-developed FPS.…”

Section: Related Workmentioning

confidence: 99%

Learning a context-aware weapon selection policy for Unreal Tournament III

Galli

Loiacono

Lanzi

2009

2009 IEEE Symposium on Computational Intelligence and Games

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Modern computer games are becoming increasingly complex and only experienced players can fully master the game controls. Accordingly, many commercial games now provide aids to simplify the player interaction. These aids are based on simple heuristics rules and cannot adapt neither to the current game situation nor to the player game style.In this paper, we suggest that supervised methods can be applied effectively to improve the quality of such game aids. In particular, we focus on the problem of developing an automatic weapon selection aid for Unreal Tournament III, a recent and very popular first person shooter (FPS). We propose a framework to (i) collect a dataset from game sessions, (ii) learn a policy to automatically select the weapon, and (iii) deploy the learned models in the game to replace the default weaponswitching aid provided in the game distribution. Our approach allows the development of weapon-switching policies that are aware of the current game context and can also imitate a particular game style.

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Section: Related Workmentioning

confidence: 99%

Learning a context-aware weapon selection policy for Unreal Tournament III

Galli

Loiacono

Lanzi

2009

2009 IEEE Symposium on Computational Intelligence and Games

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Evolutionary Computation for Reinforcement Learning

Whiteson

2012

Adaptation, Learning, and Optimization

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In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary computation is one of the most promising approaches to reinforcement learning but its success is largely restricted to off-line scenarios. In on-line scenarios, an agent must strive to maximize the reward it accrues while it is learning. Temporal difference (TD) methods, another approach to reinforcement learning, naturally excel in on-line scenarios because they have selection mechanisms for balancing the need to search for better policies (exploration) with the need to accrue maximal reward (exploitation). This paper presents a novel way to strike this balance in evolutionary methods by borrowing the selection mechanisms used by TD methods to choose individual actions and using them in evolution to choose policies for evaluation. Empirical results in the mountain car and server job scheduling domains demonstrate that these techniques can substantially improve evolution's on-line performance in stochastic domains.

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Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

Koppejan

Whiteson

2011

Evol. Intel.

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This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain’s complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks.

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Real-time imitation-based adaptation of gaming behaviour in modern computer games

Cited by 4 publications

References 2 publications

Learning a context-aware weapon selection policy for Unreal Tournament III

Learning a context-aware weapon selection policy for Unreal Tournament III

Evolutionary Computation for Reinforcement Learning

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

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