A custom-designed Spider Robot trained to walk using Deep RL in a PyBullet Simulation

Overview

SpiderBot_DeepRL

Title: Implementation of Single and Multi-Agent Deep Reinforcement Learning Algorithms for a Walking Spider Robot Authors(s): Arijit Dasgupta, Chong Yu Quan

Welcome to our project! For this project, we aim to take our SpiderBot and make it walk using deep reinforcement learning. The code is written entirely in Python 3.7.7 and the following Python libraries are required for our code to work.

pybullet==3.0.6
numpy==1.18.5
matplotlib==3.3.2
tensorflow_probability==0.11.1
seaborn==0.11.0
pandas==1.1.4
tensorflow==2.3.1

Other than this, no additional software is needed for the code to work. The PyBullet Physics Engine is used for simulation using an OpenGL GUI. In this code, we have the following : -

  • A requirement.txt for required python libraries
  • SolidWorks CADs of the SpiderBot
  • SpiderBot URDFs for the SpiderBot
  • Folders for Training Logs & Plots
  • Two saved models of the SpiderBot Agent
  • Source Code for the Deep RL Implementation
  • Training Code to train the SpiderBot with Deep RL
  • Validation Code to test trained models
  • Postprocessing Code to generate plots of training

The code supports the following 5 algorithms (with their characteristics defined):

Algorithm Agent (Actor) Policy Learning Network Actions per Time-Step Action Space State Space
MAD3QN Multiple (Decentralised) Decentralised Separate Multiple Discrete Continuous
MAA2C Multiple (Decentralised) Decentralised Separate Multiple Discrete Continuous
A2CMA Single (Centralised) Decentralised Hybrid Multiple Discrete Continuous
A2CSA Single (Centralised) Centralised Hybrid Single Discrete Continuous
DDPG Single (Centralised) Centralised Separate Multiple Continuous Continuous

We will now walk through the folders and files.

Folders

SpiderBot_CADs

This folder contains all the part and assembly files for the SpiderBot. There are options for 3-legged, 4-legged, 6-legged & 8-legged SpiderBots.

SpiderBot_URDFs

This folder contains all URDF files and associated STL files for the SpiderBot. There are options for 3-legged, 4-legged, 6-legged & 8-legged SpiderBots.

Training_Logs & Training_Plots

Folders to store csv file of training data and PDF plots of training.

Saved_Models

Contains two saved models using DDPG. The FullyTrained Model (375 episodes) is able to walk well and up to 9 metres in the forward direction. The PartiallyTrained Model (50 episodes) can move forward slightly but only to a certain extent.

Source Code

SpiderBot_Environment.py

This file has the p_gym class. This uses pybullet and loads the plane environment (no obstacles) and the SpiderBot into the physics engine. The code allows an agent to retrieve state observations for a leg or whole SpiderBot and set a target velocity for joints in the SpiderBot. Finally, the code uses information from the physics engine to determine rewards for a time step.

SpiderBot_Neural_Network.py

This file has the classes for the fully-connected neural networks used. The Tensorflow 2 API is used to develop the neural networks. Depending on the algorithm and number of SpiderBot legs, the neural networks are customised for them. There is all a call method to do a forward propagation through the neural network.

SpiderBot_Agent.py

This file is a long one, which has all the operations of the agent for all 5 algorithms. It initialises the neural networks based on the algorithm in the constructor. The class also has the functionality to update the target networks for DDPG & MAD3QN. Additionally, it has a long list of methods to apply gradients for each one of the algorithms. In these methods, the TensorFlow 2 computational graph and gradient tapes are used to help in backpropagating the loss function. Finally the class also has the functionality to save all models and load all models.

SpiderBot_Replay_Buffer.py

This file contains the replay_buffer class that handles experience replay storage and operations like logging and sampling with a batch size.

SpiderBot_Walk.py

This file contains the walk function that is actually responsible for handling all training operations. This is where all the classes interact with each other. The episodes are looped through and the SpiderBot is trained. The training-related data is logged and saved as a csv into the Training_Logs folder while the best models are saved to the Saved_Models folder during training.

SpiderBot_Postprocessing.py

This file handles the plotting post-processing operations that takes the CSV file from the Training_Logs folder and saves the plot into the Training_Plots folder.

Main Code

SpiderBot_Train_Model.py

This file allows the user to set up the training session. In this file, the user can set 3 levels of configuration for training. The general config section has options for choosing algorithms, number of legs, target location, episodes etc. The Hyperparameters config section handles all hyperparameters of the entire training process. The reward structure config provides options for all the scalar rewards. The user must set all of these configs and run the file to train the SpiderBot. TIP: not using a GUI is faster for training, especially if you use a CUDA-enabled NVIDIA GPU.

SpiderBot_Validation.py

This file allows the user to validate and test a trained model, specially made for the Professors and TAs of SpiderBot to visualise our fully trained model.

How to train a model?

Unzip the SpiderBot_URDFS.zip file into the same directory. Open up SpiderBot_Train_Model.py for editing. The most important parameter is training_name that you must define. This is unique to a particular training session and all saved models, logs and plots are based on this training_name. After that set up your General Config:

#~~~~~~~~~~~~~~~~~~~~~~~~~~~ GENERAL CONFIG ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
training_name = "insert_training_name_here"
model = "DDPG"
num_of_legs = 8 
episodes = 375
target_location = 3
use_GUI = True
do_post_process = True
save_best_model = True
save_data = True

Following that, set up the configurations for the hyperparameters:

#~~~~~~~~~~~~~~~~~~~~~~~~~~~ HYPERPARAMETER CONFIG ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
time_step_size = 120./240
upper_angle = 60
lower_angle = -60
lr_actor = 0.00005
lr_critic = 0.0001
discount_rate = 0.9
update_target = None
tau = 0.005
max_mem_size = 1000000
batch_size = 512
max_action = 10
min_action = -10
noise = 1
epsilon = 1
epsilon_decay = 0.0001
epsilon_min = 0.01

Finally, set up the configuration for the reward structure:

#~~~~~~~~~~~~~~~~~~~~~~~~~~~ REWARD STRUCTURE CONFIG ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
forward_motion_reward = 500
forward_distance_reward = 250
sideways_velocity_punishment = 500
sideways_distance_penalty = 250
time_step_penalty = 1
flipped_penalty = 500
goal_reward = 500
out_of_range_penalty = 500

Then run the python code

> python SpiderBot_Train_Model.py

How to Validate/Test our Models?

To test the fully trained model, just run SpiderBot_Validation.py.

> python SpiderBot_Validation.py

If you wish to run the other saved model, the partially trained one, you can open up SpiderBot_Validation.py and edit the training_name from DDPG_FullyTrained to DDPG_PartiallyTrained in the config section as shown:

#~~~~~~~~~~~~ VALIDATION CONFIG SETUP ~~~~~~~~~~~~#
training_name = "DDPG_PartiallyTrained"
model = "DDPG"
target_location = 8
episodes = 100000000000 # A large number is set to put the simulation on loop

Video Demonstration

Owner
Arijit Dasgupta
Arijit Dasgupta
[SDM 2022] Towards Similarity-Aware Time-Series Classification

SimTSC This is the PyTorch implementation of SDM2022 paper Towards Similarity-Aware Time-Series Classification. We propose Similarity-Aware Time-Serie

Daochen Zha 49 Dec 27, 2022
Source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree.

self-driving-car In this repository I will share the source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree. Hope this might

Andrea Palazzi 2.4k Dec 29, 2022
Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Jesper Wohlert 313 Dec 27, 2022
Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics

Location-Aware Generative Adversarial Networks (LAGAN) for Physics Synthesis This repository contains all the code used in L. de Oliveira (@lukedeo),

Deep Learning for HEP 57 Oct 22, 2022
CVPRW 2021: How to calibrate your event camera

E2Calib: How to Calibrate Your Event Camera This repository contains code that implements video reconstruction from event data for calibration as desc

Robotics and Perception Group 104 Nov 16, 2022
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency This is a official implementation of the CycleContrast introduced in

13 Nov 14, 2022
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat

Hanzhe Hu 99 Dec 12, 2022
Deep Learning Based Fasion Recommendation System for Ecommerce

Project Name: Fasion Recommendation System for Ecommerce A Deep learning based streamlit web app which can recommened you various types of fasion prod

BAPPY AHMED 13 Dec 13, 2022
This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

haifeng xia 32 Oct 26, 2022
Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set

Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set This is the repository for the Deep Learning proje

Robert Krug 3 Feb 06, 2022
Mask-invariant Face Recognition through Template-level Knowledge Distillation

Mask-invariant Face Recognition through Template-level Knowledge Distillation This is the official repository of "Mask-invariant Face Recognition thro

Fadi Boutros 35 Dec 06, 2022
Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021

This folder contains the code for 'Scalable Variational Approaches for Bayesian Causal Discovery'. Installation To install, use conda with conda env c

14 Sep 21, 2022
A collection of scripts I developed for personal and working projects.

A collection of scripts I developed for personal and working projects Table of contents Introduction Repository diagram structure List of scripts pyth

Gianluca Bianco 109 Dec 26, 2022
Linear image-to-image translation

Linear (Un)supervised Image-to-Image Translation Examples for linear orthogonal transformations in PCA domain, learned without pairing supervision. Tr

Eitan Richardson 40 Aug 31, 2022
A benchmark dataset for mesh multi-label-classification based on cube engravings introduced in MeshCNN

Double Cube Engravings This script creates a dataset for multi-label mesh clasification, with an intentionally difficult setup for point cloud classif

Yotam Erel 1 Nov 30, 2021
Towards Understanding Quality Challenges of the Federated Learning: A First Look from the Lens of Robustness

FL Analysis This repository contains the code and results for the paper "Towards Understanding Quality Challenges of the Federated Learning: A First L

3 Oct 17, 2022
ComPhy: Compositional Physical Reasoning ofObjects and Events from Videos

ComPhy This repository holds the code for the paper. ComPhy: Compositional Physical Reasoning ofObjects and Events from Videos, (Under review) PDF Pro

29 Dec 29, 2022
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision This is the repository for our Paper/Contribution to the WI2022 in Nürnber

Maximilian Harl 6 Jan 17, 2022
Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback

Online Learning Of Neural Computations From Sparse Temporal Feedback This repository is the official implementation of the NeurIPS 2021 paper Online L

Lukas Braun 3 Dec 15, 2021
Learning Continuous Image Representation with Local Implicit Image Function

LIIF This repository contains the official implementation for LIIF introduced in the following paper: Learning Continuous Image Representation with Lo

Yinbo Chen 1k Dec 25, 2022