  We propose a novel framework for multi-task reinforcement learning (MTRL).
Using a variational inference formulation, we learn policies that generalize
across both changing dynamics and goals. The resulting policies are
parametrized by shared parameters that allow for transfer between different
dynamics and goal conditions, and by task-specific latent-space embeddings that
allow for specialization to particular tasks. We show how the latent-spaces
enable generalization to unseen dynamics and goals conditions. Additionally,
policies equipped with such embeddings serve as a space of skills (or options)
for hierarchical reinforcement learning. Since we can change task dynamics and
goals independently, we name our framework Disentangled Skill Embeddings (DSE).
