Additional environments compatible with OpenAI gym

Last update: Dec 06, 2022

Related tags

Deep Learning quad-swarm-rl

Overview

Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning

A codebase for training reinforcement learning policies for quadrotor swarms. Includes:

Flight dynamics simulator forked from https://github.com/amolchanov86/gym_art and extended to support swarms of quadrotor drones
Scripts and the necessary wrappers to facilitate training of control policies with Sample Factory https://github.com/alex-petrenko/sample-factory

Paper: https://arxiv.org/abs/2109.07735

Website: https://sites.google.com/view/swarm-rl

Installation

Initialize a Python environment, i.e. with conda (Python versions 3.6-3.8 are supported):

conda create -n swarm-rl python=3.8
conda activate swarm-rl

Clone and install this repo as an editable Pip package:

git clone https://github.com/alex-petrenko/quad-swarm-rl
cd quad-swarm-rl
pip install -e .

This should pull and install all the necessary dependencies, including Sample Factory and PyTorch.

Running experiments

Train

This will run the baseline experiment. Change the number of workers appropriately to match the number of logical CPU cores on your machine, but it is advised that the total number of simulated environments is close to that in the original command:

python -m swarm_rl.train --env=quadrotor_multi --train_for_env_steps=1000000000 --algo=APPO \
--use_rnn=False \
--num_workers=36 --num_envs_per_worker=4 \
--learning_rate=0.0001 --ppo_clip_value=5.0 \
--recurrence=1 --nonlinearity=tanh --actor_critic_share_weights=False \
--policy_initialization=xavier_uniform --adaptive_stddev=False --with_vtrace=False \
--max_policy_lag=100000000 --hidden_size=256 --gae_lambda=1.00 --max_grad_norm=5.0 \
--exploration_loss_coeff=0.0 --rollout=128 --batch_size=1024 --quads_use_numba=True \
--quads_mode=mix --quads_episode_duration=15.0 --quads_formation_size=0.0 \
--encoder_custom=quad_multi_encoder --with_pbt=False --quads_collision_reward=5.0 \
--quads_neighbor_hidden_size=256 --neighbor_obs_type=pos_vel --quads_settle_reward=0.0 \
--quads_collision_hitbox_radius=2.0 --quads_collision_falloff_radius=4.0 --quads_local_obs=6 \
--quads_local_metric=dist --quads_local_coeff=1.0 --quads_num_agents=8 --quads_collision_reward=5.0 \
--quads_collision_smooth_max_penalty=10.0 --quads_neighbor_encoder_type=attention \
--replay_buffer_sample_prob=0.75 --anneal_collision_steps=300000000 --experiment=swarm_rl

Or, even better, you can use the runner scripts in swarm_rl/runs/. Runner scripts (a Sample Factory feature) are Python files that contain experiment parameters, and support features such as evaluation on multiple seeds and gridsearches.

To execute a runner script run the following command:

python -m sample_factory.runner.run --run=swarm_rl.runs.quad_multi_mix_baseline_attn --runner=processes --max_parallel=4 --pause_between=1 --experiments_per_gpu=1 --num_gpus=4

This command will start training four different seeds in parallel on a 4-GPU server. Adjust the parameters accordingly to match your hardware setup.

To monitor the experiments, go to the experiment folder, and run the following command:

tensorboard --logdir=./

Test

To test the trained model, run the following command:

python -m swarm_rl.enjoy --algo=APPO --env=quadrotor_multi --replay_buffer_sample_prob=0 --continuous_actions_sample=False --quads_use_numba=False --train_dir=PATH_TO_PROJECT/swarm_rl/train_dir --experiments_root=EXPERIMENT_ROOT --experiment=EXPERIMENT_NAME

Unit Tests

To run unit tests:

./run_tests.sh

Additional environments compatible with OpenAI gym

Related tags

Overview

Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning

Installation

Running experiments

Train

Test

Unit Tests

Owner

Zhehui Huang

This tutorial aims to learn the basics of deep learning by hands, and master the basics through combination of lectures and exercises

Code release for DS-NeRF (Depth-supervised Neural Radiance Fields)

Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Jittor 64*64 implementation of StyleGAN

Cycle Consistent Adversarial Domain Adaptation (CyCADA)

Python package to generate image embeddings with CLIP without PyTorch/TensorFlow

ML-Decoder: Scalable and Versatile Classification Head

Global Rhythm Style Transfer Without Text Transcriptions

Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization

mmdetection version of TinyBenchmark.

Contrastive Feature Loss for Image Prediction

[CVPR'21] Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation

AITUS - An atomatic notr maker for CYTUS

PyTorch implementation of ENet

object detection; robust detection; ACM MM21 grand challenge; Security AI Challenger Phase VII

Official repository for the ICCV 2021 paper: UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model.

The code for our paper submitted to RAL/IROS 2022: OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition.

A collection of resources, problems, explanations and concepts that are/were important during my Data Science journey

Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

Official implementation of Few-Shot and Continual Learning with Attentive Independent Mechanisms