Reinforcement Learning for Robot Obstacle Avoidance

[Gizmotronn]

This issue is for us to document how we will use reinforcement learning to train our rl-agent to avoid obstacles. The goal of this issue is:

• Learn more about the reward function
• Help decide what parameters we will be likely to be using/needing (also see PR #2 )
• Learn how we will train the rl-agent

A useful link to get started: https://pdfs.semanticscholar.org/0fcd/a4e464c9d55ccd9f8e8e3521c286e4b47933.pdf

[Gizmotronn]

SemanticScholar - RL-agent Obstacle Avoidance

*Abstract**

Reinforcement Learning

• Reinforcement Learning is learning how to map environment situations to actions, with the goal of maximising a reward signal/value
• It is a computational approach to learn from interaction. Learning from interaction is a foundational idea in almost all learning methods
• The agent must learn from its own experience(s)
• Exploration vs exploitation:
  • The agent must take actions that give a higher reward score (on the reward function) to get the best accumulative rewards
  • However, to find the best actions/choices in certain situations, the agent needs to try actions that it has not selected before
  • The agent has no idea what the reward will be unless it takes the action (otherwise the agent would be able to finish the program on the first try, every time)
  • The agent therefore has to exploit the best known actions to obtain rewards, while also exploring unknown options (to either increase its reward or to get further)

[Gizmotronn]

https://papers.nips.cc/paper/452-obstacle-avoidance-through-reinforcement-learning.pdf

[Gizmotronn]

These resources may be useful

[Gizmotronn]

SemanticScholar - RL-agent Obstacle Avoidance

Abstract*

Reinforcement Learning

• Reinforcement Learning is learning how to map environment situations to actions, with the goal of maximising a reward signal/value
• It is a computational approach to learn from interaction. Learning from interaction is a foundational idea in almost all learning methods
• The agent must learn from its own experience(s)

• Exploration vs exploitation:

  • The agent must take actions that give a higher reward score (on the reward function) to get the best accumulative rewards
  • However, to find the best actions/choices in certain situations, the agent needs to try actions that it has not selected before
  • The agent has no idea what the reward will be unless it takes the action (otherwise the agent would be able to finish the program on the first try, every time)
  • The agent therefore has to exploit the best known actions to obtain rewards, while also exploring unknown options (to either increase its reward or to get further)

Experiments

• The experiment from this link was not just about obstacle avoidance, it was also about wall following (i.e. the agent would attempt to stay as close to the wall as possible and "trace" its path with the wall as its target line)
• I'm not sure if we have a specific location our osr agent needs to start from, however it is not practical to have a wall-following policy as on Mars there are no walls. While we could say to the agent something along the lines of "stay at the edge of the map unless there is an obstacle that will impede you from doing so", however this would mean that in real life, with Mars being a sphere - and therefore no map edges - the system would fail. While the challenge is to get the highest reward score, which means that wall/map-edge following would be eligible, it would not do much good to the actual OSR, which is what our code would be going into. However, this article is still interesting to read and still has useful information

[Gizmotronn]

Might also want to have a look at these links:

• https://arxiv.org/abs/1811.03307
• https://ieeexplore.ieee.org/document/1026964

[Gizmotronn]

Arxiv.org - Unmanned Aerial Vehicles

https://arxiv.org/pdf/1811.03307.pdf (or above comment)

Part 1

Introduction

• To be able to avoid obstacles, UAVs (or rl-agents) need to be able to perceive the distance between itself and the obstacles

[Gizmotronn]

https://research.google/pubs/pub48418/

[Gizmotronn]

Google Research - Comparison of DRL Policies for Moving Obstacle Avoidance

Abstract

• Deep RL learns to simultaneously predict the motion of objects and corresponding avoidance actions directly from robotic sensors.

[Gizmotronn]

More resources:

• ieeexplore.ieee.org/document/1026964
• http://github.com/sichkar-valentyn/Reinforcement_Learning_In_Python