  Reinforcement learning has seen great advancements in the past five years.
The successful introduction of deep learning in place of more traditional
methods allowed reinforcement learning to scale to very complex domains
achieving super-human performance in environments like the game of Go or
numerous video games. Despite great successes in multiple domains, these new
methods suffer from their own issues that make them often inapplicable to the
real world problems. Extreme lack of data efficiency, together with huge
variance and difficulty in enforcing safety constraints, is one of the three
most prominent issues in the field. Usually, millions of data points sampled
from the environment are necessary for these algorithms to converge to
acceptable policies.
  This thesis proposes novel Generative Adversarial Imaginative Reinforcement
Learning algorithm. It takes advantage of the recent introduction of highly
effective generative adversarial models, and Markov property that underpins
reinforcement learning setting, to model dynamics of the real environment
within the internal imagination module. Rollouts from the imagination are then
used to artificially simulate the real environment in a standard reinforcement
learning process to avoid, often expensive and dangerous, trial and error in
the real environment. Experimental results show that the proposed algorithm
more economically utilises experience from the real environment than the
current state-of-the-art Rainbow DQN algorithm, and thus makes an important
step towards sample efficient deep reinforcement learning.
