https://github.com/angelmtenor/RL-ROBOT

Reinforcement Learning framework for Robotics
https://github.com/angelmtenor/RL-ROBOT

cognitive-robotics decision-making reinforcement-learning robotics ros v-rep

Last synced: 3 months ago
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Reinforcement Learning framework for Robotics

• Host: GitHub
• URL: https://github.com/angelmtenor/RL-ROBOT
• Owner: angelmtenor
• License: other
• Created: 2016-11-14T20:21:42.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2022-06-19T16:20:34.000Z (over 2 years ago)
• Last Synced: 2024-05-02T18:08:12.782Z (6 months ago)
• Topics: cognitive-robotics, decision-making, reinforcement-learning, robotics, ros, v-rep
• Language: Python
• Homepage:
• Size: 6.51 MB
• Stars: 84
• Watchers: 6
• Forks: 29
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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)](http://mapir.isa.uma.es).

 

Reference: *Towards a common implementation of reinforcement learning for multiple robotics tasks*.   [Arxiv preprint](https://arxiv.org/abs/1702.06329)   

[ScienceDirect](http://www.sciencedirect.com/science/article/pii/S0957417417307613) 



## 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

![Scenarios](images/scenarios.jpg)

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 -> /vrep_scenes`