| Summary: | In this project, we first studied the fundamental Reinforcement Learning
algorithms and implemented them to play games like CartPole and VizDoom.
We then proceeded to solve the problem of obstacle avoidance in an un-
known environment using the previously developed algorithms. The field
of reinforcement learning was inspired from biological systems and enables an
agent to acquire knowledge through a trial and error process, interacting with an
environment, based on the feedback returned from this environment. A multi-
plicity of algorithms have been developed over the last years using this approach,
with spectacular results in a plethora of fields, outperforming previous human
attempts. In the current thesis we aim to implement the most fundamental
of these RL methods, namely DQN, Double DQN, REINFORCE, Actor Critic
and Proximal Policy Optimization. Then we create a custom Reinforcement
Learning Environment consisted of a mobile robot with a camera, low range
distance sensors and a collision detection sensor, moving in an unknown area
with obstacles in random positions. We, finally, train an RL agent to explore
the environment, avoiding any obstacles on the way.
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