stable-baselines3-c
stable-baselines3-contrib
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| stable-baselines3-c | stable-baselines3-contrib | |
|---|---|---|
| 1 | 6 | |
| - | 427 | |
| - | 8.0% | |
| - | 6.7 | |
| - | 28 days ago | |
| Python | ||
| - | MIT License |
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stable-baselines3-c
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GitHub Copilot: your AI pair programmer
Transformers (GPT-3) aren't quite _supervised_, but it does require valid samples.
Agree 100% with RL being the path forward. You probably have already seen ( https://venturebeat.com/2021/06/09/deepmind-says-reinforceme... ). Personally I'm really stoked for this https://github.com/Stable-Baselines-Team/stable-baselines3-c... , which will make it a lot easier for rubes like me to use RL.
stable-baselines3-contrib
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Problem with Truncated Quantile Critics (TQC) and n-step learning algorithm.
# https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/blob/master/sb3_contrib/tqc/tqc.py :
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Understanding Action Masking in RLlib
Here's a theoretical overview and an implementation of action masking for PPO.
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PPO rollout buffer for turn-based two-player game with varying turn lengths
Simplified version of rollout collection (adapted from ppo_mask.py line 282):
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GitHub Copilot: your AI pair programmer
Transformers (GPT-3) aren't quite _supervised_, but it does require valid samples.
Agree 100% with RL being the path forward. You probably have already seen ( https://venturebeat.com/2021/06/09/deepmind-says-reinforceme... ). Personally I'm really stoked for this https://github.com/Stable-Baselines-Team/stable-baselines3-c... , which will make it a lot easier for rubes like me to use RL.
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[P] Stable-Baselines3 v1.0 - Reliable implementations of RL algorithms
But as we already have vanilla DQN and QR-DQN (in our contrib repo: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ) I think it is already a good start for off-policy discrete action algorithms. (QR-DQN is usually competitive vs DQN+extensions)
What are some alternatives?
TabNine - AI Code Completions
muzero-general - MuZero
copilot-cli - The AWS Copilot CLI is a tool for developers to build, release and operate production ready containerized applications on AWS App Runner or Amazon ECS on AWS Fargate.
githut - Github Language Statistics
pen.el - Pen.el stands for Prompt Engineering in emacs. It facilitates the creation, discovery and usage of prompts to language models. Pen supports OpenAI, EleutherAI, Aleph-Alpha, HuggingFace and others. It's the engine for the LookingGlass imaginary web browser.
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
asciinema - Terminal session recorder 📹
dreamerv2 - Mastering Atari with Discrete World Models
robot-gym - RL applied to robotics.
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.