Abstract:
Reinforcement Learning is a way of machine learning in which the training of machine
learning models takes place by training the machine (the agent) to choose a certain
action from a different set of actions in a given state from a different set of states in a
given environment and by taking that action the agent receives a reward which will help
it choose better actions in the future by maximizing this reward over time.
Reinforcement learning is making the agent learn by interacting with the environment
without the need to collect samples to train on, in traditional reinforcement learning
there is usually a Q-Table that is basically a table that consists of all possible actions as
columns and all possible states as rows, each cell (pair of action-state) has a value which
is called Q-Value which is the reward for taking that action at that state in the given
environment, However traditional Reinforcement learning is not very efficient in the
modern complex environments so comes the need of the scope of this project: Deep
Reinforcement learning.