agents
stable-baselines3
agents | stable-baselines3 | |
---|---|---|
11 | 46 | |
2,731 | 7,953 | |
0.4% | 3.1% | |
8.0 | 8.2 | |
about 1 month ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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agents
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cannot import name 'binary_weighted_focal_crossentropy' from 'keras.backend'
im trying to follow this tutorial = https://github.com/tensorflow/agents/blob/master/docs/tutorials/9_c51_tutorial.ipynb
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Trying to apply the TensorFlow agents from the examples to a custom environment
I followed the TensorFlow tutorial for agents and the multi armed bandit tutorial and now I'm trying to make one of the already implemented agents, from the examples, work on my own environment. Basically my environment exists of 5 actions and 5 observations. Applying one action i results in the same state i. One action contains another step of sending that action number to a different program via a socket and the answer from the program is interpreted for the reward. My environment seems to be working, I used the little test script below to test the observe and action functions. I know this is not a full proof but showed its atleast working.
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DD-PPO, TD3, SAC: which is the best?
Depending on what task you pick the "best" algo will vary. There are also a bunch of variations and tricks for each of those, some of which have been given new names over time. If you are working on a project I would suggest whichever one has the simplest and most extendable implementation. If you really want to compare all of them you can use libraries that have them all implemented, such as tfagents.
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Help understanding PPO training performance
I'm using a simple training loop, based on this.
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I need suggestions to improve my project
Hello everyone, I published my python project a month ago, it's a command line interface for training, tuning and reusing reinforcement learning algorithms in tensorflow 2.x. It's similar to stable-baselines, tf-agents, and not so many others. It seems like it's not getting enough attention despite the README, license, and everything else.
- xagents, a new reinforcement learning library in TF2
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tf-agents throws ValueError: Layer dense layer expects 1 input(s), but it received 4 input tensors when using custom environment with OpenAI Gym
Well it seems it doesn't flatten anything, just passes OrderedDict as input dense. Not sure but apparently it's keras that makes that a list of tensors. You can dig around places like https://github.com/tensorflow/agents/blob/v0.8.0/tf_agents/networks/network.py https://github.com/tensorflow/agents/blob/v0.8.0/tf_agents/agents/dqn/dqn_agent.py https://github.com/openai/gym/blob/master/gym/spaces/dict.py if you want to be really sure.
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[D] Choosing best parameters from an optimization
2- You could go the reinforcement learning approach by controlling these parameters using an agent. This would mean that the parameters would have to change on the fly, which I am not sure if appropriate. If so, creating a gym environment is not so hard, which would then use something like tf.agents , rlax or any other rl framework of your liking.
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"Modern" version of OpenAI's spinning up?
Not the same style and looks somehow more complicated but i want to mention tf.agents if you don't know about it already.
- Can somebody give me reinforcement learning code example.
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?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
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.
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
VMAgent - Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Gymnasium - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
habitat-api - A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators. [Moved to: https://github.com/facebookresearch/habitat-lab]
tianshou - An elegant PyTorch deep reinforcement learning library.
GPflowOpt - Bayesian Optimization using GPflow
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros