text: The authors propose a model for learning physical interaction skills through trial and error. They use end-to-end deep reinforcement learning - the DQN model - including the task goal as an input in order to to improve generalization over several tasks, and shaping the reward depending on the visual differences between the goal state and the current state. They show that the task performance of their model is better than the DQN on two simulated tasks. The paper is well-written, clarity is good, it could be slightly improved by updating the title "Toy example with Goal integration" to make it consistent with the naming "navigation task" used elsewhere. If the proposed model is new given the reviewer's knowledge, the contribution is small. The biggest change compared to the DQN model is the addition of information in the input. The authors initially claim that "In this paper, [they] study how an artificial agent can autonomously acquire this intuition through interaction with the environment", however the proposed tasks present little to no realistic physical interaction: the navigation task is a toy problem where no physics is simulated. In the stacking task, only part of the simulation actually use the physical simulation result. Given that machine learning methods are in general good at finding optimal policies that exploit simulation limitations, this problem seems a threat to the significance of this work. The proposed GDQN model shows better performance than the DQN model. However, as the authors do not provide in-depth analysis of what the network learns (e.g. by testing policies in the absence of an explicit goal), it is difficult to judge if the network learnt a meaningful representation of the world's physics. This limitation along with potential other are not discussed in the paper. Finally, more than a third (10/26) references point to Arxiv papers. Despite Arxiv definitely being an important tool for paper availability, it is not peer-reviewed and there are also work that are non-finished or erroneous. It is thus a necessary condition that all Arxiv references are replaced by the peer-reviewed material when it exist (e.g. Lerer 2016 in ICML or Denil 2016 in ICLR 2017), once again to strengthen the author's claim.