  Deep reinforcement learning (RL) algorithms have achieved great success on a
wide variety of sequential decision-making tasks. However, many of these
algorithms suffer from high sample complexity when learning from scratch using
environmental rewards, due to issues such as credit-assignment and
high-variance gradients, among others. Transfer learning, in which knowledge
gained on a source task is applied to more efficiently learn a different but
related target task, is a promising approach to improve the sample complexity
in RL. Prior work has considered using pre-trained teacher policies to enhance
the learning of the student policy, albeit with the constraint that the teacher
and the student MDPs share the state-space or the action-space. In this paper,
we propose a new framework for transfer learning where the teacher and the
student can have arbitrarily different state- and action-spaces. To handle this
mismatch, we produce embeddings which can systematically extract knowledge from
the teacher policy and value networks, and blend it into the student networks.
To train the embeddings, we use a task-aligned loss and show that the
representations could be enriched further by adding a mutual information loss.
Using a set of challenging simulated robotic locomotion tasks involving
many-legged centipedes, we demonstrate successful transfer learning in
situations when the teacher and student have different state- and
action-spaces.
