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

End to end imitation learning of dynamically unstable systems

Pixels to Controls is a widely studies topic in the field of controls and machine learning. This project aims to implement behavior based cloning and imitation learning approaches for dynamically unstable systems. The first part of this project has been implemented on ROS and Gazebo. For Golem Krang robot(shown above), an expert LQR controller has been developed which tracks a certain trajectory in simulation. In this ongoing project, next step