  Exploration in multi-task reinforcement learning is critical in training
agents to deduce the underlying MDP. Many of the existing exploration
frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single
stationary MDP and are not suitable for system identification in the multi-task
setting. We present a novel method to facilitate exploration in multi-task
reinforcement learning using deep generative models. We supplement our method
with a low dimensional energy model to learn the underlying MDP distribution
and provide a resilient and adaptive exploration signal to the agent. We
evaluate our method on a new set of environments and provide intuitive
interpretation of our results.
