MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

Related tags

Deep Learningmetadrive
Overview


MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL


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 .

or

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!

build codecov Documentation GitHub license Codacy Badge GitHub contributors

Comments
  • Reproducibility Problem

    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

    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

    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 ?

    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? `` Ekran görüntüsü 2022-03-31 114715

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

    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

    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

    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.

    image

    opened by EdAlexAguilar 4
  • Fix close and reset issue

    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

    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?

    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

    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

    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?

    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)
Owner
DeciForce: Crossroads of Machine Perception and Autonomy
Research on Unifying Machine Perception and Autonomy in Zhou Group
DeciForce: Crossroads of Machine Perception and Autonomy
GitHub Repository
Attention-based Transformation from Latent Features to Point Clouds (AAAI 2022)

Attention-based Transformation from Latent Features to Point Clouds This repository contains a PyTorch implementation of the paper: Attention-based Tr

12 Nov 11, 2022
MacroTools provides a library of tools for working with Julia code and expressions.

MacroTools.jl MacroTools provides a library of tools for working with Julia code and expressions. This includes a powerful template-matching system an

FluxML 278 Dec 11, 2022
The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022

DG-TrajGen The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022. Our Meth

Wang 25 Sep 26, 2022
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth, in ICCV 2021 (oral)

RINDNet RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth Mengyang Pu, Yaping Huang, Qingji Guan and Haibin Lin

Mengyang Pu 75 Dec 15, 2022
ObjectDetNet is an easy, flexible, open-source object detection framework

Getting started with the ObjectDetNet ObjectDetNet is an easy, flexible, open-source object detection framework which allows you to easily train, resu

5 Aug 25, 2020
Code for ICE-BeeM paper - NeurIPS 2020

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA This repository contains code to run and reproduce the experiments

Ilyes Khemakhem 65 Dec 22, 2022
TensorFlow-LiveLessons - "Deep Learning with TensorFlow" LiveLessons

TensorFlow-LiveLessons Note that the second edition of this video series is now available here. The second edition contains all of the content from th

Deep Learning Study Group 830 Jan 03, 2023
the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)

RMA-Net This repo is the implementation of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021). Paper

Wanquan Feng 205 Nov 09, 2022
MPI Interest Group on Algorithms on 1st semester 2021

MPI Algorithms Interest Group Introduction Lecturer: Steve Yan Location: TBA Time Schedule: TBA Semester: 1 Useful URLs Typora: https://typora.io Goog

Ex10si0n 13 Sep 08, 2022
Revisiting Global Statistics Aggregation for Improving Image Restoration

Revisiting Global Statistics Aggregation for Improving Image Restoration Xiaojie Chu, Liangyu Chen, Chengpeng Chen, Xin Lu Paper: https://arxiv.org/pd

MEGVII Research 128 Dec 24, 2022
Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit

STORM Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit [Install Instructions] [Paper] [Website] This package contains code

NVIDIA Research Projects 101 Dec 12, 2022
A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

ICT.MIRACLE lab 75 Dec 26, 2022
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Soubhik Sanyal 71 Dec 30, 2022
Implementing DeepMind's Fast Reinforcement Learning paper

Fast Reinforcement Learning This is a repo where I implement the algorithms in the paper, Fast reinforcement learning with generalized policy updates.

Marcus Chiam 6 Nov 28, 2022
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Creating Artificial Life with Reinforcement Learning

Although Evolutionary Algorithms have shown to result in interesting behavior, they focus on learning across generations whereas behavior could also be learned during ones lifetime.

Maarten Grootendorst 49 Dec 21, 2022
Projecting interval uncertainty through the discrete Fourier transform

Projecting interval uncertainty through the discrete Fourier transform This repo

1 Mar 02, 2022
[AAAI 2021] MVFNet: Multi-View Fusion Network for Efficient Video Recognition

MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021) Overview We release the code of the MVFNet (Multi-View Fusion Network).

Wenhao Wu 114 Nov 27, 2022
RRxIO - Robust Radar Visual/Thermal Inertial Odometry: Robust and accurate state estimation even in challenging visual conditions.

RRxIO - Robust Radar Visual/Thermal Inertial Odometry RRxIO offers robust and accurate state estimation even in challenging visual conditions. RRxIO c

Christopher Doer 64 Dec 29, 2022
FishNet: One Stage to Detect, Segmentation and Pose Estimation

FishNet FishNet: One Stage to Detect, Segmentation and Pose Estimation Introduction In this project, we combine target detection, instance segmentatio

1 Oct 05, 2022