Decision-Making in Reinforcement Learning
  In this research work, probabilistic decision-making approaches are studied,
e.g. Bayesian and Boltzmann strategies, along with various deterministic
exploration strategies, e.g. greedy, epsilon-Greedy and random approaches. In
this research work, a comparative study has been done between probabilistic and
deterministic decision-making approaches, the experiments are performed in
OpenAI gym environment, solving Cart Pole problem. This research work discusses
about the Bayesian approach to decision-making in deep reinforcement learning,
and about dropout, how it can reduce the computational cost. All the
exploration approaches are compared. It also discusses about the importance of
exploration in deep reinforcement learning, and how improving exploration
strategies may help in science and technology. This research work shows how
probabilistic decision-making approaches are better in the long run as compared
to the deterministic approaches. When there is uncertainty, Bayesian dropout
approach proved to be better than all other approaches in this research work.
