MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

Last update: Jan 04, 2023

Related tags

Deep Learning metadrive

Overview

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL

[ Documentation | Demo Video ]

MetaDrive is a driving simulator with the following key features:

Compositional: It supports generating infinite scenes with various road maps and traffic settings for the research of generalizable RL.
Lightweight: It is easy to install and run. It can run up to 300 FPS on a standard PC.
Realistic: Accurate physics simulation and multiple sensory input including Lidar, RGB images, top-down semantic map and first-person view images.

🛠 Quick Start

Install MetaDrive via:

git clone https://github.com/decisionforce/metadrive.git
cd metadrive
pip install -e .

pip install metadrive-simulator

Note that the program is tested on both Linux and Windows. Some control and display issues in MacOS wait to be solved

You can verify the installation of MetaDrive via running the testing script:

# Go to a folder where no sub-folder calls metadrive
python -m metadrive.examples.profile_metadrive

Note that please do not run the above command in a folder that has a sub-folder called ./metadrive.

🚕 Examples

Run the following command to launch a simple driving scenario with auto-drive mode on. Press W, A, S, D to drive the vehicle manually.

python -m metadrive.examples.drive_in_single_agent_env

Run the following command to launch a safe driving scenario, which includes more complex obstacles and cost to be yielded.

python -m metadrive.examples.drive_in_safe_metadrive_env

You can also launch an instance of Multi-Agent scenario as follows

python -m metadrive.examples.drive_in_multi_agent_env --env roundabout

or launch and render in pygame front end

python -m metadrive.examples.drive_in_multi_agent_env --pygame_render --env roundabout

env argument could be:

roundabout (default)
intersection
tollgate
bottleneck
parkinglot
pgmap

Run the example of procedural generation of a new map as:

python -m metadrive.examples.procedural_generation

Note that the above four scripts can not be ran in a headless machine. Please refer to the installation guideline in documentation for more information about how to launch runing in a headless machine.

Run the following command to draw the generated maps from procedural generation:

python -m metadrive.examples.draw_maps

To build the RL environment in python script, you can simply code in the OpenAI gym format as:

import metadrive  # Import this package to register the environment!
import gym

env = gym.make("MetaDrive-v0", config=dict(use_render=True))
# env = metadrive.MetaDriveEnv(config=dict(environment_num=100))  # Or build environment from class
env.reset()
for i in range(1000):
    obs, reward, done, info = env.step(env.action_space.sample())  # Use random policy
    env.render()
    if done:
        env.reset()
env.close()

🏫 Documentations

Find more details in: MetaDrive

📎 References

Working in Progress!

Comments

Reproducibility Problem

Hello,

I am trying to create custom scenarios. For that, I created a custom map similar to vis_a_small_town.py, and I am using the drive_in_multi_agent_env.py example. The environment is defined as follow:

env = envs[env_cls_name](
        {
            "use_render": True, # if not args.pygame_render else False,
            "manual_control": True,
            "crash_done": False,
            #"agent_policy": ManualControllableIDMPolicy, 
            "num_agents": total_agent_number,
            #"prefer_track_agent": "agent3",
            "show_fps": True, 
            "vehicle_config": {
                "lidar": {"num_others": total_agent_number},
                "show_lidar": False,    
            },
            "target_vehicle_configs": {"agent{}".format(i): {
                    #"spawn_lateral": i * 2,
                    "spawn_longitude": i * 10,
                    #"spawn_lane_index":0,
                    "vehicle_model": vehicle_model_list[i],
                    #"max_engine_force":1,
                    "max_speed":100,
                }
                                           for i in range(5)}
        }
    )

I have a list that consists of steering and throttle_brake values. The scenario consists of almost 1600 steps. I am assigning these values regarding step number into env.step:

o, r, d, info=env.step({
                'agent0': [agent0.steering, agent0.pedal], 
                'agent1': [agent1.steering, agent1.pedal],
                'agent2': [agent2.steering, agent2.pedal],
                'agent3': [agent3.steering, agent3.pedal],
                'agent4': [agent4.steering, agent4.pedal],
                'agent5': [agent5.steering, agent5.pedal],
                'agent6': [agent6.steering, agent6.pedal],
                'agent7': [agent7.steering, agent7.pedal],
                'agent8': [agent8.steering, agent8.pedal],
                'agent9': [agent9.steering, agent9.pedal],
                }
            )

At the end of the list for vehicle commands, the counter for loop set the zero, and commands are reused. Also, vehicles' locations, speeds, and headings are set to initial values stored in the dictionary:

def initilize_vehicles(env):

    global total_agent_number

    for i in range (total_agent_number):
        agent_str = "agent" + str(i)
        env.vehicles[agent_str].set_heading_theta(vehicles_initial_values[agent_str]['initial_heading_theta'])
        env.vehicles[agent_str].set_position([vehicles_initial_values[agent_str]['initial_position_x'],vehicles_initial_values[agent_str]['initial_position_y']]) # it is x,y from the first block of the map
        env.vehicles[agent_str].set_velocity(env.vehicles[agent_str].velocity_direction, vehicles_initial_values[agent_str]['initial_velocity'])

I want to reproduce the scenario and test my main algorithm. However, the problem is vehicles do not act in the same way in every run of scenario. I checked my commands for vehicle using:

env.vehicles["agent0"].steering,env.vehicles["agent0"].throttle_brake

Vehicle commands are the same for each repetition of scenarios.

When I don't use a loop and start the MetaDrive from the terminal, I mostly see the same action from cars. I tested almost 10 times. But in loop case, cars start to act differently after the first loop.

Reproducibility is a huge concern for me. Is it something about the physic engine? Are there any configuration parameters for the engine?

Thanks!!

opened by BedirhanKeskin 9

Add more description for Waymo dataset
What changes do you make in this PR?

Please describe why you create this PR

Checklist

[ ] I have merged the latest main branch into current branch.

[ ] I have run bash scripts/format.sh before merging.

Please use "squash and merge" mode.
opened by pengzhenghao 6
Constant FPS mode

Is there a way to set constant fps mode? I tried env.engine.force_fps.toggle(): then env.engine.force_fps.fps is showing 50 but visualisation is showing 10-16 fps in the top right corner. Is there any other way? Thanks in advance!

opened by bbenja 6
What is neighbours_distance ?

Hello, What is the neighbours_distance and difference with the distance definition inside Lidar? They are inside MULTI_AGENT_METADRIVE_DEFAULT_CONFIG I guess the unit is in meters? ``

opened by BedirhanKeskin 5
I encountered an error at an unknown location during runtime. Hello

Successfully registered the following environments: ['MetaDrive-validation-v0', 'MetaDrive-10env-v0', 'MetaDrive-100envs-v0', 'MetaDrive-1000envs-v0', 'SafeMetaDrive-validation-v0', 'SafeMetaDrive-10env-v0', 'SafeMetaDrive-100envs-v0', 'SafeMetaDrive-1000envs-v0', 'MARLTollgate-v0', 'MARLBottleneck-v0', 'MARLRoundabout-v0', 'MARLIntersection-v0', 'MARLParkingLot-v0', 'MARLMetaDrive-v0']. Known pipe types: wglGraphicsPipe (all display modules loaded.)

opened by shushushulian 4
about panda3d

when i run "python -m metadrive.examples.drive_in_safe_metadrive_env", set use_render=true the output: Successfully registered the following environments: ['MetaDrive-validation-v0', 'MetaDrive-10env-v0', 'MetaDrive-100envs-v0', 'MetaDrive-1000envs-v0', 'SafeMetaDrive-validation-v0', 'SafeMetaDrive-10env-v0', 'SafeMetaDrive-100envs-v0', 'SafeMetaDrive-1000envs-v0', 'MARLTollgate-v0', 'MARLBottleneck-v0', 'MARLRoundabout-v0', 'MARLIntersection-v0', 'MARLParkingLot-v0', 'MARLMetaDrive-v0']. Known pipe types: glxGraphicsPipe (1 aux display modules not yet loaded.)

opened by benicioolee 4
RGB Camera returns time-buffered grayscale images
Hi, I am running a vanilla MetaDriveEnv with the rgb camera sensor.

veh_config = dict( image_source="rgb_camera", rgb_camera=(IMG_DIM, IMG_DIM))

I wanted to see the images the sensor was producing, so was saving a few of them: I am using: from PIL import Image

action = np.array([0,0]) obs, reward, done, info = env.step(action) img = Image.fromarray(np.array(obs['image']*256,np.uint8)) img.save(f"test.jpeg")

I noticed that the images all looked grayscale. And upon further inspection I found the following behavior:: Suppose we want (N,N) images, which should be represented as arrays of size (N,N,3). Step 0: image[:,:,0] = zeros(N,N) ; image[:,:,1] = zeros(N,N) ; image[:,:,2] = zeros(N,N) Step 1: image[:,:,0] = zeros(N,N) ; image[:,:,1] = zeros(N,N) ; image[:,:,2] = m1 Step 2: image[:,:,0] = zeros(N,N) ; image[:,:,1] = m1 ; image[:,:,2] = m2 Step 3: image[:,:,0] = m1 ; image[:,:,1] = m2 ; image[:,:,2] = m3

where m1, m2, m3 are (N,N) matrices.

So, the images are in reality displaying 3 different timesteps with the color channels taking the time info (R=t-2, G=t-1, B=t) . That is why the images look mostly gray, since the values are identical almost everywhere – except where we expect some movement (contours) where we see that lines look colorful and strange.

Apologies if this is expected behavior, and I just had some configuration incorrect.
opened by EdAlexAguilar 4
Fix close and reset issue
What changes do you make in this PR?

Please describe why you create this PR

close #191

Checklist

[x] I have merged the latest main branch into current branch.

[x] I have run bash scripts/format.sh before merging.

Please use "squash and merge" mode.
opened by pengzhenghao 4
Selection of parameter in Rllib training for SAC agent in MetaDriveEnv and SafeMetaDriveEnv

What is the proper buffer size / batch size / entropy coefficient for SAC to reproduce the results? I find it hard to reproduce results in SafeMetaDriveEnv. In https://arxiv.org/pdf/2109.12674.pdf, does the reported success rate of SAÇ in Table1 refer to the training success rate (and no collision, i.e. safe_rl_env=True)?

opened by HenryLHH 4
Suggestion to run multiple instance in parallel?
First of all I would like to express my gratitude on this great project. I really like the feature-rich and lightweight nature of MetaDrive as a driving simulator for reinforcement learning.

I am wondering what is the recommended way to run multiple MetaDrive instances in parallel (each one with a single ego-car agent)? This seem to be a common use case for reinforcement learning training. I am currently running a batch of MetaDrive simulator with each of them in wrapped in a process, which does seem to have overheads of extra resource and communication/synchronization.

Another problem I encountered when running multiple instances (say, 60 instances on single machine) in their own process is that I will get a lot of warning like this:

ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated ALSA lib pulse.c:242:(pulse_connect) PulseAudio: Unable to connect: Connection terminated

I guess this has something to do with audio. This happens even though I am running the TopDown environment, which should not involve sound. Did you see those warning when running multiple instances as well?

Also, is there a plan to have a vectorized batch version?

Thanks!
opened by breakds 4
Rendering FPS of example script is too low

When I run python -m metadrive.examples.drive_in_single_agent_env I found the fps is about 4 fps I used the nvidia-smi command and found that my 2060gpu was not occupied.

I also can find some warnings about: WARNING:root: It seems you don't install our cython utilities yet! Please reinstall MetaDrive via .........

opened by feidieufo 4
Errors when running metadrive.tests.scripts.generate_video_for_image_obs

In metadrive directory, I ran python -m metadrive.tests.scripts.generate_video_for_image_obs , then it reported an error as below: python -m metadrive.tests.scripts.generate_video_for_image_obs

Successfully registered the following environments: ['MetaDrive-validation-v0', 'MetaDrive-10env-v0', 'MetaDrive-100envs-v0', 'MetaDrive-1000envs-v0', 'SafeMetaDrive-validation-v0', 'SafeMetaDrive-10env-v0', 'SafeMetaDrive-100envs-v0', 'SafeMetaDrive-1000envs-v0', 'MARLTollgate-v0', 'MARLBottleneck-v0', 'MARLRoundabout-v0', 'MARLIntersection-v0', 'MARLParkingLot-v0', 'MARLMetaDrive-v0']. :display(warning): Unable to load libpandagles2.so: No error. Known pipe types: (all display modules loaded.) Traceback (most recent call last): File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/Users/queenie/Documents/metadrive/metadrive/tests/scripts/generate_video_for_image_obs.py", line 157, in env.reset() File "/Users/queenie/Documents/metadrive/metadrive/envs/base_env.py", line 333, in reset self.lazy_init() # it only works the first time when reset() is called to avoid the error when render File "/Users/queenie/Documents/metadrive/metadrive/envs/base_env.py", line 234, in lazy_init self.engine = initialize_engine(self.config) File "/Users/queenie/Documents/metadrive/metadrive/engine/engine_utils.py", line 11, in initialize_engine cls.singleton = cls(env_global_config) File "/Users/queenie/Documents/metadrive/metadrive/engine/base_engine.py", line 28, in init EngineCore.init(self, global_config) File "/Users/queenie/Documents/metadrive/metadrive/engine/core/engine_core.py", line 135, in init super(EngineCore, self).init(windowType=self.mode) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 339, in init self.openDefaultWindow(startDirect = False, props=props) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 1021, in openDefaultWindow self.openMainWindow(*args, **kw) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 1056, in openMainWindow self.openWindow(*args, **kw) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 766, in openWindow win = func() File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 752, in callbackWindowDict = callbackWindowDict) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 818, in _doOpenWindow self.makeDefaultPipe() File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 648, in makeDefaultPipe "No graphics pipe is available!\n" File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/directnotify/Notifier.py", line 130, in error raise exception(errorString) Exception: No graphics pipe is available! Your Config.prc file must name at least one valid panda display library via load-display or aux-display. (drivemeta) ➜ metadrive git:(main) python -m metadrive.tests.scripts.generate_video_for_image_obs Successfully registered the following environments: ['MetaDrive-validation-v0', 'MetaDrive-10env-v0', 'MetaDrive-100envs-v0', 'MetaDrive-1000envs-v0', 'SafeMetaDrive-validation-v0', 'SafeMetaDrive-10env-v0', 'SafeMetaDrive-100envs-v0', 'SafeMetaDrive-1000envs-v0', 'MARLTollgate-v0', 'MARLBottleneck-v0', 'MARLRoundabout-v0', 'MARLIntersection-v0', 'MARLParkingLot-v0', 'MARLMetaDrive-v0']. :display(warning): Unable to load libpandagles2.so: No error. Known pipe types: (all display modules loaded.) Traceback (most recent call last): File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/Users/queenie/Documents/metadrive/metadrive/tests/scripts/generate_video_for_image_obs.py", line 157, in env.reset() File "/Users/queenie/Documents/metadrive/metadrive/envs/base_env.py", line 333, in reset self.lazy_init() # it only works the first time when reset() is called to avoid the error when render File "/Users/queenie/Documents/metadrive/metadrive/envs/base_env.py", line 234, in lazy_init self.engine = initialize_engine(self.config) File "/Users/queenie/Documents/metadrive/metadrive/engine/engine_utils.py", line 11, in initialize_engine cls.singleton = cls(env_global_config) File "/Users/queenie/Documents/metadrive/metadrive/engine/base_engine.py", line 28, in init EngineCore.init(self, global_config) File "/Users/queenie/Documents/metadrive/metadrive/engine/core/engine_core.py", line 135, in init super(EngineCore, self).init(windowType=self.mode) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 339, in init self.openDefaultWindow(startDirect = False, props=props) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 1021, in openDefaultWindow self.openMainWindow(*args, **kw) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 1056, in openMainWindow self.openWindow(*args, **kw) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 766, in openWindow win = func() File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 752, in callbackWindowDict = callbackWindowDict) File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 818, in _doOpenWindow self.makeDefaultPipe() File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/showbase/ShowBase.py", line 648, in makeDefaultPipe "No graphics pipe is available!\n" File "/Users/queenie/anaconda3/envs/drivemeta/lib/python3.7/site-packages/direct/directnotify/Notifier.py", line 130, in error raise exception(errorString) Exception: No graphics pipe is available! Your Config.prc file must name at least one valid panda display library via load-display or aux-display.

opened by YouSonicAI 2
opencv-python-headless in requirements seems to create conflict?

Sometimes it has different version to opencv-python can cause issue. It is only used in top-down-rendering. Can we change this dependency to opencv-python?

opened by pengzhenghao 0

Releases(MetaDrive-0.2.6.0)

MetaDrive-0.2.6.0(Nov 24, 2022)
MetaDrive-0.2.6.0

This new release is for scenario-based autonomous driving research. We provide several new features like:

Top-down view for Waymo Map

New navigation system/reward function for real scenarios

Efficiency improvement

Release the updated Waymo dataset at https://github.com/metadriverse/metadrive-scenario/releases

Source code(tar.gz)
Source code(zip)
metadrive-simulator-0.2.6.0.tar.gz(46.49 MB)
metadrive_simulator-0.2.6.0-py3-none-any.whl(46.67 MB)
MetaDrive-0.2.5(May 3, 2022)
We now release MetaDrive-0.2.5, enabling the following features:

Bug fixing in MARL environments, except ParkingLotEnv

Replaying Waymo data and loading IDM Traffic in real environments

Providing 1000+ Waymo scenarios split into training and test set to benchmark the performance of generalizability in real scenarios

For using real data, please note:

Waymo motion dataset provides almost 30,000 cases collected in the real world. Due to the space limitation, we only release a part of them.

Each training dataset contains a different number of scenarios randomly sampled from all cases.

Validation set has 100 cases

DO NOT violate the license terms of Waymo dataset

Source code(tar.gz)
Source code(zip)
1000_training.zip(728.39 MB)
100_training.zip(74.83 MB)
300_training.zip(217.94 MB)
500_training.zip(360.33 MB)
generated_1385_training.zip(746.92 MB)
generated_no_threshold_data.zip(1260.61 MB)
validation.zip(74.68 MB)
MetaDrive-0.2.4(Feb 20, 2022)

MetaDrive-0.2.4

Recently, we add full support to Waymo and Argoverse datasets. Also, this draft contains several patches about bug-fixing and performance optimization.
Source code(tar.gz)
Source code(zip)
metadrive-simulator-0.2.4.tar.gz(44.69 MB)
metadrive_simulator-0.2.4-py3-none-any.whl(44.85 MB)
MetaDrive-0.2.3(Sep 5, 2021)
We release MetaDrive-0.2.3 now!

You can install this version via pip install metadrive-simulator or download the packages directly.

Note: It is supposed to be available on both Windows and Unix-like systems, but the new Macbook with the M1 chip may encounter a bug.

The following features are supported:

Infinite maps for benchmarking the generalization ability of a learned policy

Pre-defined MARL environments to study traffic, a common self-driven particle system

Importing real data to metadrive by replaying surrounding vehicles' trajectories and converting the data structure of the real maps automatically

Source code with a clear framework and can be easily extended

Basic API for creating your own environment by using class BaseObject, class BaseManager and class BaseEnv

Inheriting from class BasePolicy to implement rule-based policy, neural policy, and the mixed one.

Depth camera, RGB camera, and Lidar can be accessed to get raw data, and for users focusing on using the perception results metadrive also provide them with API to get surrounding information directly

Source code(tar.gz)
Source code(zip)
human_traj_100_new.json(128.99 MB)
metadrive-simulator-0.2.3.tar.gz(44.31 MB)
metadrive_simulator-0.2.3-py3-none-any.whl(44.44 MB)

Owner

DeciForce: Crossroads of Machine Perception and Autonomy

Research on Unifying Machine Perception and Autonomy in Zhou Group

GitHub Repository

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

33 Dec 07, 2022

Predictive Maintenance LSTM

Predictive-Maintenance-LSTM - Predictive maintenance study for Complex case study, we've obtained failure causes by operational error and more deeply by design mistakes.

1 Dec 31, 2021

Official source code to CVPR'20 paper, "When2com: Multi-Agent Perception via Communication Graph Grouping"

When2com: Multi-Agent Perception via Communication Graph Grouping This is the PyTorch implementation of our paper: When2com: Multi-Agent Perception vi

34 Nov 09, 2022

Fully Convlutional Neural Networks for state-of-the-art time series classification

Deep Learning for Time Series Classification As the simplest type of time series data, univariate time series provides a reasonably good starting poin

572 Dec 23, 2022

a reimplementation of Holistically-Nested Edge Detection in PyTorch

pytorch-hed This is a personal reimplementation of Holistically-Nested Edge Detection [1] using PyTorch. Should you be making use of this work, please

375 Dec 06, 2022

An MQA (Studio, originalSampleRate) identifier for lossless flac files written in Python.

An MQA (Studio, originalSampleRate) identifier for "lossless" flac files written in Python.

10 Oct 03, 2022

Self-Supervised Learning with Kernel Dependence Maximization

Self-Supervised Learning with Kernel Dependence Maximization This is the code for SSL-HSIC, a self-supervised learning loss proposed in the paper Self

29 Dec 29, 2022

Source code for the paper "Periodic Traveling Waves in an Integro-Difference Equation With Non-Monotonic Growth and Strong Allee Effect"

Source code for the paper "Periodic Traveling Waves in an Integro-Difference Equation With Non-Monotonic Growth and Strong Allee Effect" by Michael Ne

1 Apr 19, 2022

MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

Related tags

Overview

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL

🛠 Quick Start

🚕 Examples

🏫 Documentations

📎 References

Comments

What changes do you make in this PR?

Checklist

What changes do you make in this PR?

Checklist

Releases(MetaDrive-0.2.6.0)

MetaDrive-0.2.6.0(Nov 24, 2022)

MetaDrive-0.2.6.0

MetaDrive-0.2.5(May 3, 2022)

We now release MetaDrive-0.2.5, enabling the following features:

For using real data, please note:

MetaDrive-0.2.4(Feb 20, 2022)

MetaDrive-0.2.4

MetaDrive-0.2.3(Sep 5, 2021)

We release MetaDrive-0.2.3 now!

The following features are supported:

Owner

DeciForce: Crossroads of Machine Perception and Autonomy

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Predictive Maintenance LSTM

Official source code to CVPR'20 paper, "When2com: Multi-Agent Perception via Communication Graph Grouping"

Fully Convlutional Neural Networks for state-of-the-art time series classification

a reimplementation of Holistically-Nested Edge Detection in PyTorch

An MQA (Studio, originalSampleRate) identifier for lossless flac files written in Python.

Self-Supervised Learning with Kernel Dependence Maximization

Source code for the paper "Periodic Traveling Waves in an Integro-Difference Equation With Non-Monotonic Growth and Strong Allee Effect"

Utility tools for the "Divide and Remaster" dataset, introduced as part of the Cocktail Fork problem paper

【Arxiv】Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation

A Survey on Deep Learning Technique for Video Segmentation

Simple, but essential Bayesian optimization package

M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images

code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`

Multi Task Vision and Language

Stacked Recurrent Hourglass Network for Stereo Matching

Simple image captioning model - CLIP prefix captioning.

An image classification app boilerplate to serve your deep learning models asap!

Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes