A Simple Example for Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env

Related tags

Deep Learningtensorflowdaggertorcstensorflow-tutorialstensorlayerimitation-learninggym-torcstorcs-env
Overview

Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env

This repository implements a simple algorithm for imitation learning: DAGGER. In this example, the agent only learns to control the steer [-1, 1], the speed is computed automatically in gym_torcs.TorcsEnv.

Requirements

  1. Ubuntu (I only test on this)
  2. Python 3
  3. TensorLayer and TensorFlow
  4. Gym-Torcs

Setting Up

It is a little bit boring to set up the environment, but any incorrect configurations will lead to FAILURE. After installing Gym-Torcs, please follow the instructions to confirm everything work well:

  • Open a terminal:

    • Run sudo torcs -vision to start a game
    • Race --> Practice --> Configure Race: set the driver to scr_server 1 instead of player
    • Open Torcs server by selecting Race --> Practice --> New Race: This should result that Torcs keeps a blue screen with several text information.

  • Open another terminal:

    • Run python snakeoil3_gym.py on another terminal, it will shows how the fake AI control the car.
    • Press F2 to see the driver view.

  • Set image size to 64x64x3:

    • The model is trained on 64x64 RGB observation.
    • Run sudo torcs -vision to start a game
    • Options --> Display --> select 64x64 --> Apply

Usage

Make sure everything above work well and then run:

  • python dagger.py

It will start a Torcs server at the beginning of every episode, and terminate the server when the car crashs or the speed is too low. Note that, the self-contained gym_torcs.py is modified from Gym-Torcs, you can try different settings (like default speed, terminated speed) by modifying it.

Results

After Episode 1, the car crashes after 315 steps.

After Episode 3, the car does not crash anymore !!!

The number of steps and episodes might vary depending on the parameters initialization.

ENJOY !

You might also like...
PyTorch implementation of SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching
PyTorch implementation of SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching

SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching This is the official PyTorch implementation of SMODICE: Versatile Offline I

Jason Ma 14 Aug 30, 2022
Neon-erc20-example - Example of creating SPL token and wrapping it with ERC20 interface in Neon EVM

Example of wrapping SPL token by ERC2-20 interface in Neon Requirements Install

null 7 Mar 28, 2022
Example-custom-ml-block-keras - Custom Keras ML block example for Edge Impulse

Custom Keras ML block example for Edge Impulse This repository is an example on

Edge Impulse 8 Nov 2, 2022
Python-kafka-reset-consumergroup-offset-example - Python Kafka reset consumergroup offset example

Python Kafka reset consumergroup offset example This is a simple example of how

Willi Carlsen 1 Feb 16, 2022
Pytorch code for "State-only Imitation with Transition Dynamics Mismatch" (ICLR 2020)

This repo contains code for our paper State-only Imitation with Transition Dynamics Mismatch published at ICLR 2020. The code heavily uses the RL mach

null 20 Sep 8, 2022
[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral)
[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral)

PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision Kehong Gong*, Bingbing Li*, Jianfeng Zhang*, Ta

null 256 Dec 28, 2022
Learning to Estimate Hidden Motions with Global Motion Aggregation

Learning to Estimate Hidden Motions with Global Motion Aggregation (GMA) This repository contains the source code for our paper: Learning to Estimate

Shihao Jiang (Zac) 221 Dec 18, 2022
Official repository for the CVPR 2021 paper "Learning Feature Aggregation for Deep 3D Morphable Models"

Deep3DMM Official repository for the CVPR 2021 paper Learning Feature Aggregation for Deep 3D Morphable Models. Requirements This code is tested on Py

null 38 Dec 27, 2022
A pytorch reproduction of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.
A pytorch reproduction of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.

A PyTorch Reproduction of HCN Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation. Ch

Guyue Hu 210 Dec 31, 2022
Comments
  • About the convergence and overfit

    About the convergence and overfit

    Hi, thanks for your job and I rewrite it using Keras in the attitude of learning. And I use your recommended hyper-parameters but when I run my program it's apt to overfit. Later on, I change the hyper-parameters , add BN and explicit initialization function of each layer. But it's still overfitting and the car runs 700 steps at the best time but still can't go through the all track. I have spent more than two weeks to tune it. I'm so confused of the tuning, why the same hyper-parameters can't achieve the same result? Why the network is so apt to overfit? For convenience, I update my programmer imitationLearning.py Can you give me some idea? Than you in advance.

    opened by marooncn 0
Releases(0.1)
Owner
Hao
Assistant Professor @ Peking University
Hao
GitHub Repository https://github.com/zsdonghao/tensorlayer
FastFace: Lightweight Face Detection Framework

Light Face Detection using PyTorch Lightning

Ömer BORHAN 75 Dec 05, 2022
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

MI-AOD Language: 简体中文 | English Introduction This is the code for Multiple Instance Active Learning for Object Detection (The PDF is not available tem

Tianning Yuan 269 Dec 21, 2022
This project is the PyTorch implementation of our CVPR 2022 paper:

Requirements and Dependency Install PyTorch with CUDA (for GPU). (Experiments are validated on python 3.8.11 and pytorch 1.7.0) (For visualization if

Lei Huang 23 Nov 29, 2022
The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.

SuperGen The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding. Requirements Before running, you

Yu Meng 38 Dec 12, 2022
Augmented CLIP - Training simple models to predict CLIP image embeddings from text embeddings, and vice versa.

Train aug_clip against laion400m-embeddings found here: https://laion.ai/laion-400-open-dataset/ - note that this used the base ViT-B/32 CLIP model. S

Peter Baylies 55 Sep 13, 2022
PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021

HyperSPN This repository contains code for the paper: HyperSPNs: Compact and Expressive Probabilistic Circuits "HyperSPNs: Compact and Expressive Prob

8 Nov 08, 2022
CMP 414/765 course repository for Spring 2022 semester

CMP414/765: Artificial Intelligence Spring2021 This is the GitHub repository for course CMP 414/765: Artificial Intelligence taught at The City Univer

ch00226855 4 May 16, 2022
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting Recent progress in neural forecasting instigated significant improvements in the

Cristian Challu 82 Jan 04, 2023
EGNN - Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch

EGNN - Pytorch Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch. May be eventually used for Alphafold2 replication. This

Phil Wang 259 Jan 04, 2023
Extension to fastai for volumetric medical data

FAIMED 3D use fastai to quickly train fully three-dimensional models on radiological data Classification from faimed3d.all import * Load data in vari

Keno 26 Aug 22, 2022
Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge Introduction SentiLARE is a sentiment-aware pre-trained language

74 Dec 30, 2022
Dual Attention Network for Scene Segmentation (CVPR2019)

Dual Attention Network for Scene Segmentation(CVPR2019) Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu Introduction W

Jun Fu 2.2k Dec 28, 2022
PyTorch and Tensorflow functional model definitions

functional-zoo Model definitions and pretrained weights for PyTorch and Tensorflow PyTorch, unlike lua torch, has autograd in it's core, so using modu

Sergey Zagoruyko 590 Dec 22, 2022
On Nonlinear Latent Transformations for GAN-based Image Editing - PyTorch implementation

On Nonlinear Latent Transformations for GAN-based Image Editing - PyTorch implementation On Nonlinear Latent Transformations for GAN-based Image Editi

Valentin Khrulkov 22 Oct 24, 2022
Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation ICCV 2021 Harsh Rangwani, Arihant Jain*, Sumukh K Aithal*, R. Ve

Video Analytics Lab -- IISc 13 Dec 28, 2022
Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Optimizing Dense Retrieval Model Training with Hard Negatives Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma This repo provi

Jingtao Zhan 99 Dec 27, 2022
Per-Pixel Classification is Not All You Need for Semantic Segmentation

MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation Bowen Cheng, Alexander G. Schwing, Alexander Kirillov [arXiv] [Proj

Facebook Research 1k Jan 08, 2023
The reference baseline of final exam for XMU machine learning course

Mini-NICO Baseline The baseline is a reference method for the final exam of machine learning course. Requirements Installation we use /python3.7 /torc

JoaquinChou 3 Dec 29, 2021
MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL [ Documentation | Demo Video ] MetaDrive is a driving simulator with the following

DeciForce: Crossroads of Machine Perception and Autonomy 276 Jan 04, 2023
UMPNet: Universal Manipulation Policy Network for Articulated Objects

UMPNet: Universal Manipulation Policy Network for Articulated Objects Zhenjia Xu, Zhanpeng He, Shuran Song Columbia University Robotics and Automation

Columbia Artificial Intelligence and Robotics Lab 33 Dec 03, 2022