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. ScienceDirect(updated) Arxiv preprint
- Python >= 3.4
- numpy >= 1.11
- matplotlib >= 1.5
- tkinter
sudo apt-get install python-tk
Tested on Ubuntu 14.04 and 16.04 (64 bits).
import exp
import rlrobot
exp.ENVIRONMENT_TYPE = "MODEL"
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 version: V-REP PRO EDU V3.3.2
-
Use default values of
remoteApiConnections.txt
portIndex1_port = 19997 portIndex1_debug = false portIndex1_syncSimTrigger = true
-
Activate threaded rendering (recommended):
system/usrset.txt -> threadedRenderingDuringSimulation = 1
Recommended simulation settings for V-REP scenes (already set in the provide ones):
- 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 -> (open any scene for RL-ROS)