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In this work we describe a novel deep reinforcement learning architecturethat allows multiple actions to be selected at every time-step in an efficientmanner. Multi-action policies allow complex behaviours to be learnt that wouldotherwise be hard to achieve when using single action selection techniques. Weuse both imitation learning and temporal difference (TD) reinforcement learning(RL) to provide a 4x improvement in training time and 2.5x improvement inperformance over single action selection TD RL. We demonstrate the capabilitiesof this network using a complex in-house 3D game. Mimicking the behavior of theexpert teacher significantly improves world state exploration and allows theagents vision system to be trained more rapidly than TD RL alone. This initialtraining technique kick-starts TD learning and the agent quickly learns tosurpass the capabilities of the expert.

Imitation Learning with Concurrent Actions in 3D Games