This repository contains my projects for Udacity's Deep Reinforcement Learning Nanodegree.

In this project, I trained an agent to navigate and collect bananas (yellow and blue) in a large, square world. If an agent collects a yellow banana, it will get a reward of +1. Otherwise, it will get a reward of -1 for collecting a blue banana. Therefore, the goal of an agent is to collect as many yellow bananas as possible while avoiding blue bananas.

• Link to notebook

In this project, I trained an agent to maintain its position at the target location for as many time steps as possible. In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible.

• Link to notebook

In this project, I trained 02 agent to maintain control rackets to bounce a ball over a net for as many time steps as possible. In this environment, two agents control rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01. Thus, the goal of each agent is to keep the ball in play.

• Link to notebook