AirSim VS open_spiel

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.

Deep mind actually has a repository where they applied this algorithm for incomplete-knowledge games. You could use it for reference: https://github.com/deepmind/open_spiel/tree/master/open_spiel/python/algorithms

+1. You can also find an implementation of Clobber and AlphaZero (and many other basic RL algorithms) in OpenSpiel: https://github.com/deepmind/open_spiel

You can find a lot of code online if you look, and many of these applied to Poker. There's a general implementation of both in Python and C++ in OpenSpiel, with some examples applied to small poker games. It's nice code to learn from because the algorithms operate over generic game descriptions, so there aren't game-specific design choices mixed up with the implementation of the algorithms, and you can create your own poker game and just run them on it.

And many other additions and improvements. See all the details here: https://github.com/deepmind/open_spiel/releases/tag/v1.3

I would checkout the openspiel package. It's main focus is RL in games (multi-agent environments). You'll find RL examples there and games that are small enough to solve without deep RL. There's also a wide range of environments from fully cooperative to adversarial zero-sum.

Hi, you can check out OpenSpiel: https://github.com/deepmind/open_spiel/

as for algorithms , openspiel repository has few implementations some of these are not related to imperfect information games , and others are not for multiagent environment and others are tabular algorithms .

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:

I've had some success using Action Masking - you can refer to here https://github.com/deepmind/open_spiel/blob/120420a74a69354d64c10b51cd129d4587f9f325/open_spiel/python/algorithms/dqn.py but for DQN you need to mask out q values for invalid actions (as well as masking them during prediction). In my case I'm able to place my mask in the observation so can fetch it quite easily during prediction but if that's not possible you could query it from the environment and store it in the replay buffer (like they do in the link I shared)

Take a look at this simple implementation: https://github.com/deepmind/open_spiel/blob/master/open_spiel/algorithms/minimax.cc