angelmtenor / RL-ROBOT

Reinforcement Learning framework for Robotics

RL-ROBOT

Ángel Martínez-Tenor - 2016

This repository provides a Reinforcement Learning framework in Python from the Machine Perception and Intelligent Robotics research group (MAPIR).

Reference: Towards a common implementation of reinforcement learning for multiple robotics tasks.   Arxiv preprint    ScienceDirect

Getting Started

Setup

• Create a python environment and install the requirements. e.g. using conda:

conda create -n rlrobot python=3.10
conda activate rlrobot
pip install -r requirements.txt
# tkinter: sudo apt install python-tk 

Run

• Execute python run_custom_exp.py (content below)

import exp
import rlrobot

exp.ENVIRONMENT_TYPE = "MODEL"   # "VREP" for V-REP simulation
exp.TASK_ID = "wander_1k"
exp.FILE_MODEL = exp.TASK_ID + "_model"

exp.ALGORITHM = "TOSL"
exp.ACTION_STRATEGY = "QBIASSR"
 
exp.N_REPETITIONS = 1
exp.N_EPISODES = 1
exp.N_STEPS = 60 * 60

exp.DISPLAY_STEP = 500

rlrobot.run()

• Full set of parameters available in exp.py

• Tested on Ubuntu 14,16 ,18, 20 (64 bits)

V-REP settings:

Tested: V-REP PRO EDU V3.3.2 / V3.5.0

1. Use default values of remoteApiConnections.txt

   portIndex1_port 		= 19997
   portIndex1_debug 		= false
   portIndex1_syncSimTrigger 	= true
   

2. Activate threaded rendering (recommended): system/usrset.txt -> threadedRenderingDuringSimulation = 1

Recommended simulation settings for V-REP scenes:

• Simulation step time: 50 ms (default)
• Real-Time Simulation: Enabled
• Multiplication factor: 3.00 (required CPU >= i3 3110m)

Execute V-REP (./vrep.sh on linux). File -> Open Scene -> <RL-ROBOT path>/vrep_scenes