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

Implementation for Shape from Polarization for Complex Scenes in the Wild

AugLy is a data augmentations library that currently supports four modalities (audio, image, text & video) and over 100 augmentations

Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators

Self-Supervised Image Denoising via Iterative Data Refinement

Contextual Attention Network: Transformer Meets U-Net

Incorporating Transformer and LSTM to Kalman Filter with EM algorithm

This repository includes different versions of the prescribed-time controller as Simulink blocks and MATLAB script codes for engineering applications.

Mixed Neural Likelihood Estimation for models of decision-making

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

Create animations for the optimization trajectory of neural nets

TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

Pytorch implementation of CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation"

Gym environment for FLIPIT: The Game of "Stealthy Takeover"

Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21)

Video Autoencoder: self-supervised disentanglement of 3D structure and motion

MIM: MIM Installs OpenMMLab Packages

Adversarial Graph Augmentation to Improve Graph Contrastive Learning

Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Implementation of average- and worst-case robust flatness measures for adversarial training.