tmrl
acme
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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 |
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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 :)
acme
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Fast and hackable frameworks for RL research
I'm tired of having my 200m frames of Atari take 5 days to run with dopamine, so I'm looking for another framework to use. I haven't been able to find one that's fast and hackable, preferably distributed or with vectorized environments. Anybody have suggestions? seed-rl seems promising but is archived (and in TF2). sample-factory seems super fast but to the best of my knowledge doesn't work with replay buffers. I've been trying to get acme working but documentation is sparse and many of the features are broken.
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Recomendations of framework/library for MARL
Recently dm-acme also added support for multi-agent environments. Acme: https://github.com/deepmind/acme
- Have you used any good DRL library?
- Is there a way to get PPO controlled agents to move a little more gracefully?
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Worthwhile to convert custom env to be dm_env compatible?
Can anyone speak to their experience using acme (https://github.com/deepmind/acme) and by extension dm_env (https://github.com/deepmind/dm_env)? I'm wondering if it would be worthwhile for me to invest the time into converting my custom environment (which loosely follows the standard RL setup) over to this format.
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[D] Physics and Reinforcement Learning - Discussion of Deepmind's work
acme/acme/agents/tf/mpo at master · deepmind/acme · GitHub
- Applied resources in Pytorch?
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Spec for RL agent implementation?
Acme has a slightly different one: https://github.com/deepmind/acme which includes specs for agents, buffers etc. It is very general. You can see their component description here: https://github.com/deepmind/acme/blob/master/docs/components.md
What are some alternatives?
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.