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

Deep learning model, heat map, data prepo

Iterative Normalization: Beyond Standardization towards Efficient Whitening

Multi-Objective Loss Balancing for Physics-Informed Deep Learning

Implementation of " SESS: Self-Ensembling Semi-Supervised 3D Object Detection" (CVPR2020 Oral)

Pytorch implementation for "Adversarial Robustness under Long-Tailed Distribution" (CVPR 2021 Oral)

This repo contains the pytorch implementation for Dynamic Concept Learner (accepted by ICLR 2021).

TLXZoo - Pre-trained models based on TensorLayerX

Deep Learning to Create StepMania SM FIles

MaRS - a recursive filtering framework that allows for truly modular multi-sensor integration

This is the official implementation code repository of Underwater Light Field Retention : Neural Rendering for Underwater Imaging (Accepted by CVPR Workshop2022 NTIRE)

Frigate - NVR With Realtime Object Detection for IP Cameras

AI4Good project for detecting waste in the environment

This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"

A hybrid framework (neural mass model + ML) for SC-to-FC prediction

Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Dynamical movement primitives (DMPs), probabilistic movement primitives (ProMPs), spatially coupled bimanual DMPs.

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects

Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.

Punctuation Restoration using Transformer Models for High-and Low-Resource Languages

This is the first released system towards complex meters` detection and recognition, which is implemented by computer vision techniques.