DeepRL_Path_Planner

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DeepRL_Path_Planner

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A framework for path-planing and obstacle avoidance using Deep Reinforcement Learning Techniques.

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# DeepRL_Path_Planner
A framework for path-planing and obstacle avoidance using Deep Reinforcement Learning Techniques.

## Introduction:
The absence robust and generalized path planner has been one of the greatest hindrances in realizing any form of cyber-physical system as a part of our daily lives, coexisting and enhancing the standard of our livelihood and efficiency. The recent years have seen Reinforcement Learning emerging as a viable solution for any tasks related to sequential decision making. In this project, we plan to give this problem more of a Learning approach than the traditional Planning approach. We also notice that this essentially is a learning problem as an agent should learn to generalize enough and adapt to the unpredictability and uncertainties put forward by real-world environments.Thus, we propose to build a Path planning  Deep Reinforcement Learning Based Agent that could tackle dynamic obstacles and as well could navigate through a non-stationary environment to reach a given goal state. We believe that an agent of this sort could act as a High-level motion planner in various robotics and navigation-related tasks.

## A Brief Overview:
### Our Environment:
We selected a seemingly simple grid-world to first check the feasibility of our approach. Our need was rightly satisfied by a third party Gym environment named “MiniGrid”.

## On Going work:
* The overall problem was divided into subtasks and were solved by individual Deep Q-Networks.
* Experimenting with applying a hierarchical structure to the sub tasks and combine with Hierarhial Reinforcement Learning.
* Also simulataneously developing Evolutionary Strategy based solution so as to solve the above problem from a Evolutionary Reinforcement Learning based approach.

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