Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation

09/22/2022
by   Jack Saunders, et al.
0

Reinforcement learning (RL) is an agent-based approach for teaching robots to navigate within the physical world. Gathering data for RL is known to be a laborious task, and real-world experiments can be risky. Simulators facilitate the collection of training data in a quicker and more cost-effective manner. However, RL frequently requires a significant number of simulation steps for an agent to become skilful at simple tasks. This is a prevalent issue within the field of RL-based visual quadrotor navigation where state dimensions are typically very large and dynamic models are complex. Furthermore, rendering images and obtaining physical properties of the agent can be computationally expensive. To solve this, we present a simulation framework, built on AirSim, which provides efficient parallel training. Building on this framework, Ape-X is modified to incorporate decentralised training of AirSim environments to make use of numerous networked computers. Through experiments we were able to achieve a reduction in training time from 3.9 hours to 11 minutes using the aforementioned framework and a total of 74 agents and two networked computers. Further details including a github repo and videos about our project, PRL4AirSim, can be found at https://sites.google.com/view/prl4airsim/home

READ FULL TEXT

page 4

page 6

research
07/08/2022

High Performance Simulation for Scalable Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning experiments and open-source training ...
research
08/17/2023

ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents

Robots have been successfully used to perform tasks with high precision....
research
06/03/2020

Interferobot: aligning an optical interferometer by a reinforcement learning agent

Limitations in acquiring training data restrict potential applications o...
research
04/12/2023

Exploiting Intrinsic Stochasticity of Real-Time Simulation to Facilitate Robust Reinforcement Learning for Robot Manipulation

Simulation is essential to reinforcement learning (RL) before implementa...
research
05/10/2022

VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation

Ultrasound (US) is one of the most common medical imaging modalities sin...
research
10/03/2020

Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban

Intelligent robots need to achieve abstract objectives using concrete, s...
research
03/03/2023

POPGym: Benchmarking Partially Observable Reinforcement Learning

Real world applications of Reinforcement Learning (RL) are often partial...

Please sign up or login with your details

Forgot password? Click here to reset