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Tmrl Alternatives
Similar projects and alternatives to tmrl
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stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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PettingZoo
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
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softlearning
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
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InfluxDB
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wandb
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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stable-baselines3-contrib
Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
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SaaSHub
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better tmrl alternative or higher similarity.
tmrl reviews and mentions
Posts with mentions or reviews of tmrl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-09.
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Problem with Truncated Quantile Critics (TQC) and n-step learning algorithm.
Hi all! I'm implementing a TQC with n-step learning in Trackmania (I forked original repo from here: https://github.com/trackmania-rl/tmrl, my modified version here: https://github.com/Pheoxis/AITrackmania/tree/main). It compiles, but I am pretty sure that I implemented n-step learning incorrectly, but as a beginner I don't know what I did wrong. Here's my code before implementing n-step algorithm: https://github.com/Pheoxis/AITrackmania/blob/main/tmrl/custom/custom_algorithms.py. If anyone checked what I did wrong, I would be very grateful. I will also attach some plots from my last training and outputs from printed lines (print.txt), maybe it will help :) If you need any additional information feel free to ask.
- Training an unbeatable AI in Trackmania [video]
- Can you beat trackmania AI?
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Python RL Environments on Windows
I don't know if it fits your need : https://github.com/trackmania-rl/tmrl
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New to reinforcement learning.
Hi, if you are gonna train a deep RL algorithm on a real robot and you are a beginner, I suggest you try out tmrl. This will allow you to try out a readily available algorithm (Soft Actor-Critic) in real-time on a real video-game (TrackMania), as real-world-like proxy for all the concerns you will encounter on real robot, and to rather easily develop your own robot-learning pipeline from there for your own robot. The repo has a huge tutorial exactly for this purpose.
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AI Learns Mario Kart Wii (Rainbow DQN)
I see, and how did you handle the simulator and dynamics? Did you "step" the game or did you capture screenshots with constant time interval in real-time? I am asking because tmrl uses the second option in TrackMania, which makes the approach generalizable to all video games including MarioKart, but so far we met no such success training CNNs with the Soft Actor-Critic familly. The setting is a bit more difficult because it uses continuous input inckuding the gas and break (I suppose you always send gas to the maximum?) and we don't use punishments for collisions and this kind of tricks, but still, if it works that well in your setting I think it should work similarly in ours.
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Have you used any good DRL library?
I am very disappointed these guys don't cite tmrl :D
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osu!
If you just want to make a Gym environment, focus on rtgym. You will want to have a way of retrieving observations, say, raw images for instance. You can do this with pywin32 as done here. You will also want to grab a reward signal, which will probably be the most challenging thing to do because you will have to compute that from screenshots since you don't have access to the game internals (in fact you could because the game is opensource, but I assume you don't want to go down this path). If there is something like a score counter that's not moving somewhere, I suggest you capture that and read the numbers individually with the 1-NN algorithm (which is done in the un-supported "trackmania nation forever" versions of the tmrl encironment btw if you need help doing that). Then you will need to input controls. I see that osu is controller by the mouse and keyboard, there are several solutions for that. You can use pywin32 I think, pyautogui, keyboard, and probably others. Once you have these basic building blocks, it will be fairly straightforward to use the rtgym API in order to build you real time Gym environment.
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Is replay buffer can remove "done"?
Sometimes it is more than okay, it may be necessary. For instance in tmrl we do exactly that, because we are in a partially observable environment where we cannot say whether the next state will be terminal or not, and where what we try to actually optimize is an infinite sum of discounted rewards.
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[P] DeepForSpeed: A self driving car in Need For Speed Most Wanted with just a single ConvNet to play ( inspired by nvidia )
Cool project. Shameless self-advertising here but you can use vgamepad to control the game with a virtual gamepad instead of key presses, which enables analog policies. We do this in TrackMania :)
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www.influxdata.com | 25 Apr 2024
Stats
Basic tmrl repo stats
11
422
6.3
2 days ago
trackmania-rl/tmrl is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of tmrl is Python.
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