  Deep reinforcement learning has proven to be successful for learning tasks in
simulated environments, but applying same techniques for robots in real-world
domain is more challenging, as they require hours of training. To address this,
transfer learning can be used to train the policy first in a simulated
environment and then transfer it to physical agent. As the simulation never
matches reality perfectly, the physics, visuals and action spaces by necessity
differ between these environments to some degree. In this work, we study how
general video games can be directly used instead of fine-tuned simulations for
the sim-to-real transfer. Especially, we study how the agent can learn the new
action space autonomously, when the game actions do not match the robot
actions. Our results show that the different action space can be learned by
re-training only part of neural network and we obtain above 90% mean success
rate in simulation and robot experiments.
