  Humans are masters at quickly learning many complex tasks, relying on an
approximate understanding of the dynamics of their environments. In much the
same way, we would like our learning agents to quickly adapt to new tasks. In
this paper, we explore how model-based Reinforcement Learning (RL) can
facilitate transfer to new tasks. We develop an algorithm that learns an
action-conditional, predictive model of expected future observations, rewards
and values from which a policy can be derived by following the gradient of the
estimated value along imagined trajectories. We show how robust policy
optimization can be achieved in robot manipulation tasks even with approximate
models that are learned directly from vision and proprioception. We evaluate
the efficacy of our approach in a transfer learning scenario, re-using
previously learned models on tasks with different reward structures and visual
distractors, and show a significant improvement in learning speed compared to
strong off-policy baselines. Videos with results can be found at
https://sites.google.com/view/ivg-corl19
