Imitation Learning with Concurrent Actions in 3D Games
This addresses the challenge of achieving complex behaviors in 3D games for AI agents, though it is incremental as it builds on existing imitation and reinforcement learning techniques.
The paper tackles the problem of learning complex behaviors in 3D games by enabling multiple concurrent actions per time-step, resulting in a 4x improvement in training time and 2.5x improvement in performance over single-action methods.
In this work we describe a novel deep reinforcement learning architecture that allows multiple actions to be selected at every time-step in an efficient manner. Multi-action policies allow complex behaviours to be learnt that would otherwise be hard to achieve when using single action selection techniques. We use both imitation learning and temporal difference (TD) reinforcement learning (RL) to provide a 4x improvement in training time and 2.5x improvement in performance over single action selection TD RL. We demonstrate the capabilities of this network using a complex in-house 3D game. Mimicking the behavior of the expert teacher significantly improves world state exploration and allows the agents vision system to be trained more rapidly than TD RL alone. This initial training technique kick-starts TD learning and the agent quickly learns to surpass the capabilities of the expert.