DI-engine
godot_rl_agents
| DI-engine | godot_rl_agents | |
|---|---|---|
| 3 | 5 | |
| 2,553 | 748 | |
| 5.7% | - | |
| 8.7 | 9.1 | |
| 9 days ago | 21 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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DI-engine
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Anyone have experience with DI-Engine?
I posted a while back asking people what frameworks they were using for RL research. Recently i stumbled upon DI-Engine which looks promising! Actively maintained, with a diverse set of algorithms already implemented.
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TransformerXL + PPO Baseline + MemoryGym
DI Engine
- Struggling with algorithm generality? Try DI engine; here is the solution
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.
What are some alternatives?
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
Miniworld - Simple and easily configurable 3D FPS-game-like environments for reinforcement learning
tianshou - An elegant PyTorch deep reinforcement learning library.
episodic-transformer-memory-ppo - Clean baseline implementation of PPO using an episodic TransformerXL memory
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
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.
Gymnasium - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
on-policy - This is the official implementation of Multi-Agent PPO (MAPPO).
avalon - A 3D video game environment and benchmark designed from scratch for reinforcement learning research