  Training robots to perceive, act and communicate using multiple modalities
still represents a challenging problem, particularly if robots are expected to
learn efficiently from small sets of example interactions. We describe a
learning approach as a step in this direction, where we teach a humanoid robot
how to play the game of noughts and crosses. Given that multiple multimodal
skills can be trained to play this game, we focus our attention to training the
robot to perceive the game, and to interact in this game. Our multimodal deep
reinforcement learning agent perceives multimodal features and exhibits verbal
and non-verbal actions while playing. Experimental results using simulations
show that the robot can learn to win or draw up to 98% of the games. A pilot
test of the proposed multimodal system for the targeted game---integrating
speech, vision and gestures---reports that reasonable and fluent interactions
can be achieved using the proposed approach.
