tmrl
awesome-reinforcement-learning-lib
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tmrl | awesome-reinforcement-learning-lib | |
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11 | 1 | |
408 | 275 | |
9.1% | - | |
6.3 | 0.6 | |
about 1 month ago | about 1 year ago | |
Python | ||
MIT License | - |
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tmrl
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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.
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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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Have you used any good DRL library?
I am very disappointed these guys don't cite tmrl :D
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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 :)
awesome-reinforcement-learning-lib
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Have you used any good DRL library?
I found this summary on github, and it looks pretty complete: https://github.com/wwxFromTju/awesome-reinforcement-learning-lib
What are some alternatives?
drqv2 - DrQ-v2: Improved Data-Augmented Reinforcement Learning
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.
acme - A library of reinforcement learning components and agents
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.
vgamepad - Virtual XBox360 and DualShock4 gamepads in python
alf - Agent Learning Framework https://alf.readthedocs.io
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
awesome-decision-transformer - A curated list of Decision Transformer resources (continually updated)
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
taichi - Productive, portable, and performant GPU programming in Python.