  Deep reinforcement learning agents have recently been successful across a
variety of discrete and continuous control tasks; however, they can be slow to
train and require a large number of interactions with the environment to learn
a suitable policy. This is borne out by the fact that a reinforcement learning
agent has no prior knowledge of the world, no pre-existing data to depend on
and so must devote considerable time to exploration. Transfer learning can
alleviate some of the problems by leveraging learning done on some source task
to help learning on some target task. Our work presents an algorithm for
initialising the hidden feature representation of the target task. We propose a
domain adaptation method to transfer state representations and demonstrate
transfer across domains, tasks and action spaces. We utilise adversarial domain
adaptation ideas combined with an adversarial autoencoder architecture. We
align our new policies' representation space with a pre-trained source policy,
taking target task data generated from a random policy. We demonstrate that
this initialisation step provides significant improvement when learning a new
reinforcement learning task, which highlights the wide applicability of
adversarial adaptation methods; even as the task and label/action space also
changes.
