Behavioural Cloning in VizDoom
This work addresses improving human-like behavior in video game agents for gaming applications, but it is incremental as it builds on existing imitation learning methods.
The paper tackled training autonomous agents to play Doom 2 using Imitation Learning from pixel data, achieving performance on par with average human players and outperforming the worst players, while providing stronger human-like behavioral traits compared to Reinforcement Learning.
This paper describes methods for training autonomous agents to play the game "Doom 2" through Imitation Learning (IL) using only pixel data as input. We also explore how Reinforcement Learning (RL) compares to IL for humanness by comparing camera movement and trajectory data. Through behavioural cloning, we examine the ability of individual models to learn varying behavioural traits. We attempt to mimic the behaviour of real players with different play styles, and find we can train agents that behave aggressively, passively, or simply more human-like than traditional AIs. We propose these methods of introducing more depth and human-like behaviour to agents in video games. The trained IL agents perform on par with the average players in our dataset, whilst outperforming the worst players. While performance was not as strong as common RL approaches, it provides much stronger human-like behavioural traits to the agent.