nle
stable-baselines3
nle | stable-baselines3 | |
---|---|---|
15 | 46 | |
932 | 7,953 | |
0.4% | 3.1% | |
3.7 | 8.2 | |
8 days ago | 6 days ago | |
C | Python | |
GNU General Public License v3.0 or later | MIT License |
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nle
- What if we set GPT-4 free in Minecraft?
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Voyager: An LLM-powered learning agent in Minecraft
precisely, I really hope someone does Nethack next and leverages the learning environment that's already customized for it.
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Analyzer for Nethack idea - problem with getting data from another program
You should look at The Nethack Learning Environment.
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[D] We're the Meta AI research team behind CICERO, the first AI agent to achieve human-level performance in the game Diplomacy. We’ll be answering your questions on December 8th starting at 10am PT. Ask us anything!
There's quite a few open-source Reinforcement Learning challenges that you can explore with modest amounts of compute in order to build some experience training RL models, for example the Nethack Learning Environment, Atari, Minigrid, etc. For me personally, I had only worked in NLP / dialogue for years but got into RL by implementing Random Network Distillation models for NetHack. It's a fun area that definitely has its own unique challenges vs other domains. -AM
- Facebook AI which plays NetHack
- The NetHack Learning Environment
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Hacker News top posts: Nov 12, 2022
The NetHack Learning Environment\ (2 comments)
stable-baselines3
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Sim-to-real RL pipeline for open-source wheeled bipeds
The latest release (v3.0.0) of Upkie's software brings a functional sim-to-real reinforcement learning pipeline based on Stable Baselines3, with standard sim-to-real tricks. The pipeline trains on the Gymnasium environments distributed in upkie.envs (setup: pip install upkie) and is implemented in the PPO balancer. Here is a policy running on an Upkie:
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[P] PettingZoo 1.24.0 has been released (including Stable-Baselines3 tutorials)
PettingZoo 1.24.0 is now live! This release includes Python 3.11 support, updated Chess and Hanabi environment versions, and many bugfixes, documentation updates and testing expansions. We are also very excited to announce 3 tutorials using Stable-Baselines3, and a full training script using CleanRL with TensorBoard and WandB.
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[Question] Why there is so few algorithms implemented in SB3?
I am wondering why there is so few algorithms in Stable Baselines 3 (SB3, https://github.com/DLR-RM/stable-baselines3/tree/master)? I was expecting some algorithms like ICM, HIRO, DIAYN, ... Why there is no model-based, skill-chaining, hierarchical-RL, ... algorithms implemented there?
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Stable baselines! Where my people at?
Discord is more focused, and they have a page for people who wants to contribute https://github.com/DLR-RM/stable-baselines3/blob/master/CONTRIBUTING.md
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SB3 - NotImplementedError: Box([-1. -1. -8.], [1. 1. 8.], (3,), <class 'numpy.float32'>) observation space is not supported
Therefore, I debugged this error to the ReplayBuffer that was imported from `SB3`. This is the problem function -
- Exporting an A2C model created with stable-baselines3 to PyTorch
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
Have you ever wanted to use dm-control with stable-baselines3? Within Reinforcement learning (RL), a number of APIs are used to implement environments, with limited ability to convert between them. This makes training agents across different APIs highly difficult, and has resulted in a fractured ecosystem.
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Stable-Baselines3 v1.8 Release
Changelog: https://github.com/DLR-RM/stable-baselines3/releases/tag/v1.8.0
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
Great project! One question though, is there any reason why you are not using existing RL models instead of creating your own, such as stable baselines?
- Is stable-baselines3 compatible with gymnasium/gymnasium-robotics?
What are some alternatives?
wa-tunnel - Tunneling Internet traffic over Whatsapp
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
LeanQt - LeanQt is a stripped-down Qt version easy to build from source and to integrate with an application.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
BotHack - BotHack – A Nethack Bot Framework
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
dcss-ai-wrapper - An API for Dungeon Crawl Stone Soup for Artificial Intelligence research.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
tianshou - An elegant PyTorch deep reinforcement learning library.
Voyager - An Open-Ended Embodied Agent with Large Language Models
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros