Deep Reinforcement Learning Agents

Deep Reinforcement Learning based control of complex robotic agents

Deep Q Networks for exploration of an autonomous agent: In this project, an agent has to start from scratch in a previously unknown UnityML Enviornment and learn to navigate the enviornment by collecting the maximum amount of reward(yellow bananas) and avoid bad reward(blue bananas). A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. The agent previously
Habitat Point Goal Navigation

Embodied Visual Navigation in Habitat

The aim of this work is to solve the embodied point goal navigation task in photo-realistic, indoor environments using Habitat. In this task, a virtual agent (robot) starts at a random position in an unknown environment. The agent is given the coordinates of a goal location. Primary aim of the agent is to navigate to the goal while taking the most optimal path. This is not a trivial task in