AirSim VS gym

Have created all widgets, code compile successfully but have problem with replay spectator. Im using plugin AirSim https://github.com/microsoft/AirSim it is plugin that simulate drone. It has own gamemode AirsimGamemode (here is c++ file https://github.com/microsoft/AirSim/tree/main/Unreal/Plugins/AirSim/Source) that spawn drone at start. Problem is that I cant assign BP_PC_Spectator there. So I created own gamemode that I thought will override AirSimGameMode. It kinda did, it spawned drone on start but replay spectator widget show record screen, but it is not shown on play replay screen and I cannot move there as well.

Hi, I implemented path planner as node in ROS. Now I want to try the route planner in the AirSim simulator. During path execution, I want to get outputs from sensors such as GPS, IMU and Lidar. AirSim come with build in wrapper (https://github.com/microsoft/AirSim/tree/main/ros/src/airsim_ros_pkgs) that create topic and services once launched. Wrapper create two nodes, one to obtain sensors data and one to control drone. Wrapper is build in AirSim directory and path planer is in another. Is it possible to share msg so I can call and subscribe to wrapper topics in my planner node ? Or do I have to write msg for every topic, service I want to use ?

And there are also some open-source projects you could join, instead of starting a new one. This one looks interesting, haven't tried it though: https://github.com/Microsoft/AirSim

In 2017 Microsoft created AirSim, an open-source simulation platform for AI research and experimentation with drones and cars. This July, Microsoft announced the upcoming release of a new simulation platform and the archive of the original 2017 AirSim.

AirSim: https://github.com/microsoft/AirSim

AirSim

I've been trying for a while now and I started thinking it may not be possible. If anyone has managed to train a self-driving car simulator using openai gym on google colab(preferably), or on any remote server (AWS, GCP, ...) please let me know. So far, I tried carla, airsim, svl, deepdrive and they are all equally useless unless run locally with a gui. I'd really appreciate if someone suggests some way that actually can make it possible.

A co-founder announced they disbanded their robots team a couple years ago: https://venturebeat.com/business/openai-disbands-its-robotic...

That was the same time they depreciated OpenAI Gym: https://github.com/openai/gym

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

When I browse fetch_env.py, I have a question about the following code snippet:

Nvm, this works for me '!pip install setuptools==65.5.0' Source: https://github.com/openai/gym/issues/3176

how would this interact/compare with https://github.com/openai/gym?

If you'd like to learn more about reinforcement learning or play with a number of samples in controlled environments, I highly recommend you look at the documentation for OpenAI's Gym library and particularly the basic usage page. OpenAI's Gym provides a standardized environment for performing reinforcement learning on classic Atari games and a few other platforms and should be an educational resource. If you'd like a more detailed example, check out this tutorial on Paperspace's blog.

Frozen Lake is an OpenAI Gym environment in which an agent is rewarded for traversing a frozen surface from a start position to a goal position without falling through any perilous holes in the ice.

typically a more relaxed approach is taken via reinforcement learning, but it requires that you can simulate the game via a given gamestate. take a look at e.g. https://www.gymlibrary.dev/