godot_rl_agents
Ray
godot_rl_agents | Ray | |
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5 | 43 | |
748 | 31,179 | |
- | 1.8% | |
9.1 | 10.0 | |
22 days ago | about 21 hours ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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godot_rl_agents
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TransformerXL + PPO Baseline + MemoryGym
Thanks! It really depends on the task that you want to implement. But in general, sticking to the standard gymnasium API is important. If you want to implement a 2D environment then PyGame is promising. If it's more like a game, check out Unity ML-Agents or Godot RL Agents. Anything simpler can also be just pure python code. You also need to carefully design your observation space, action space and reward function. My advice is to explore design choices of related environments.
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An orb learns to dodge obstacles, collect other orbs and reach the end platform by itself using PPO RL AI
1. You will need to setup a virtual environment in python, install the module with pip, and run the "gdrl" command. Then to use it with godot, simply add a "sync" node provided with the plugin to the tree and it will take care of all your agents and their communication with the python module. The agent needs to be in the "AGENT" group and have 3 compulsory variables and some compulsory functions. You can check it out here: Custom Environment .
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Godot Engine Release Management: 4.0 and beyond
I really want to play with Godot RL Agents but I want to do vision based learning and that’s a bit down their road map. If anyone wants to contribute to add that feature I’d love you!
https://github.com/edbeeching/godot_rl_agents
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Build custom 3D gridworld environments
Check out Godot RL Agents as an alternative to Unity.
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[P] Introducing Godot RL Agents
We are proud to announce the release v0.1 of the Godot RL Agents framework, a Deep Reinforcement Learning interface for the Godot Game Engine.
Ray
- Ray: Unified framework for scaling AI and Python applications
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Open Source Advent Fun Wraps Up!
22. Ray | Github | tutorial
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Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Custom Models
Training times for GSM8k are mentioned here: https://github.com/ray-project/ray/tree/master/doc/source/te...
- Ray – an open source project for scaling AI workloads
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Methods to keep agents inside grid world.
Here's a reference from RLlib that points to docs and an example, and here's one from one of my projects that includes all my own implementations
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TransformerXL + PPO Baseline + MemoryGym
RLlib
- Is dynamic action masking possible in Rllib?
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AWS re:Invent 2022 Recap | Data & Analytics services
⦿ AWS Glue Data Quality - Automatic data quality rule recommendations based on your data AWS Glue for Ray - Data integration with Ray (ray.io), a popular new open-source compute framework that helps you scale Python workloads
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Think about it for a second
https://ray.io (just dropping the link)
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Elixir Livebook now as a desktop app
I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.
By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.
Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding (https://github.com/elixir-nx/explorer/pull/282 and https://github.com/elixir-nx/explorer/pull/286, tldr 20+ seconds down to 3).
[0] https://github.com/ray-project/ray
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
optuna - A hyperparameter optimization framework
Miniworld - Simple and easily configurable 3D FPS-game-like environments for reinforcement learning
episodic-transformer-memory-ppo - Clean baseline implementation of PPO using an episodic TransformerXL memory
Faust - Python Stream Processing
DI-engine - OpenDILab Decision AI Engine
gevent - Coroutine-based concurrency library for Python
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Gymnasium - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)