tmrl VS acme

Compare tmrl vs acme and see what are their differences.

tmrl

Reinforcement Learning for real-time applications - host of the TrackMania Roborace League (by trackmania-rl)

acme

A library of reinforcement learning components and agents (by google-deepmind)
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tmrl acme
11 11
408 3,351
9.1% 1.1%
6.3 5.8
about 1 month ago 17 days ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

tmrl

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.
  • Problem with Truncated Quantile Critics (TQC) and n-step learning algorithm.
    4 projects | /r/reinforcementlearning | 9 Dec 2023
    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.
  • New to reinforcement learning.
    3 projects | /r/reinforcementlearning | 7 Nov 2022
    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.
  • Have you used any good DRL library?
    5 projects | /r/reinforcementlearning | 7 Jun 2022
    I am very disappointed these guys don't cite tmrl :D
  • Is replay buffer can remove "done"?
    2 projects | /r/reinforcementlearning | 1 Apr 2022
    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.
  • [P] DeepForSpeed: A self driving car in Need For Speed Most Wanted with just a single ConvNet to play ( inspired by nvidia )
    4 projects | /r/MachineLearning | 19 Mar 2022
    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 :)

acme

Posts with mentions or reviews of acme. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-08.

What are some alternatives?

When comparing tmrl and acme you can also consider the following projects:

drqv2 - DrQ-v2: Improved Data-Augmented Reinforcement Learning

dm_env - A Python interface for reinforcement learning environments

Mava - 🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

MPO - Pytorch implementation of "Maximum a Posteriori Policy Optimization" with Retrace for Discrete gym environments

tonic - Tonic RL library

gym - A toolkit for developing and comparing reinforcement learning algorithms.

selfhosted-apps-docker - Guide by Example

tm-dashboard - Dashboard for Trackmania displaying a bunch of vehicle information on screen.

wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

epymarl - An extension of the PyMARL codebase that includes additional algorithms and environment support

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.