  Understanding physical phenomena is a key component of human intelligence and
enables physical interaction with previously unseen environments. In this
paper, we study how an artificial agent can autonomously acquire this intuition
through interaction with the environment. We created a synthetic block stacking
environment with physics simulation in which the agent can learn a policy
end-to-end through trial and error. Thereby, we bypass to explicitly model
physical knowledge within the policy. We are specifically interested in tasks
that require the agent to reach a given goal state that may be different for
every new trial. To this end, we propose a deep reinforcement learning
framework that learns policies which are parametrized by a goal. We validated
the model on a toy example navigating in a grid world with different target
positions and in a block stacking task with different target structures of the
final tower. In contrast to prior work, our policies show better generalization
across different goals.
