  We investigate a paradigm in multi-task reinforcement learning (MT-RL) in
which an agent is placed in an environment and needs to learn to perform a
series of tasks, within this space. Since the environment does not change,
there is potentially a lot of common ground amongst tasks and learning to solve
them individually seems extremely wasteful. In this paper, we explicitly model
and learn this shared structure as it arises in the state-action value space.
We will show how one can jointly learn optimal value-functions by modifying the
popular Value-Iteration and Policy-Iteration procedures to accommodate this
shared representation assumption and leverage the power of multi-task
supervised learning. Finally, we demonstrate that the proposed model and
training procedures, are able to infer good value functions, even under low
samples regimes. In addition to data efficiency, we will show in our analysis,
that learning abstractions of the state space jointly across tasks leads to
more robust, transferable representations with the potential for better
generalization. this shared representation assumption and leverage the power of
multi-task supervised learning. Finally, we demonstrate that the proposed model
and training procedures, are able to infer good value functions, even under low
samples regimes. In addition to data efficiency, we will show in our analysis,
that learning abstractions of the state space jointly across tasks leads to
more robust, transferable representations with the potential for better
generalization.
