  Recently, the Deep Planning Network (PlaNet) approach was introduced as a
model-based reinforcement learning method that learns environment dynamics
directly from pixel observations. This architecture is useful for learning
tasks in which either the agent does not have access to meaningful states (like
position/velocity of robotic joints) or where the observed states significantly
deviate from the physical state of the agent (which is commonly the case in
low-cost robots in the form of backlash or noisy joint readings). PlaNet, by
design, interleaves phases of training the dynamics model with phases of
collecting more data on the target environment, leading to long training times.
In this work, we introduce Robo-PlaNet, an asynchronous version of PlaNet. This
algorithm consistently reaches higher performance in the same amount of time,
which we demonstrate in both a simulated and a real robotic experiment.
