  Deep reinforcement learning has over the past few years shown great potential
in learning near-optimal control in complex simulated environments with little
visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have
shown outstanding performance in a variety of tasks, including Atari 2600,
MuJoCo, and Roboschool test suite. While these algorithms are fundamentally
different, both suffer from high variance, low sample efficiency, and
hyperparameter sensitivity that in practice, make these algorithms a no-go for
critical operations in the industry.
  On the other hand, model-based reinforcement learning focuses on learning the
transition dynamics between states in an environment. If these environment
dynamics are adequately learned, a model-based approach is perhaps the most
sample efficient method for learning agents to act in an environment optimally.
The traits of model-based reinforcement are ideal for real-world environments
where sampling is slow and for mission-critical operations. In the warehouse
industry, there is an increasing motivation to minimise time and to maximise
production. Currently, autonomous agents act suboptimally using handcrafted
policies for significant portions of the state-space.
  In this paper, we present The Dreaming Variational Autoencoder v2 (DVAE-2), a
model-based reinforcement learning algorithm that increases sample efficiency,
hence enable algorithms with low sample efficiency function better in
real-world environments. We introduce Deep Warehouse, a simulated environment
for industry-near testing of autonomous agents in grid-based warehouses.
Finally, we illustrate that DVAE-2 improves the sample efficiency for the Deep
Warehouse compared to model-free methods.
