agents
rlalgorithms-tf2
agents | rlalgorithms-tf2 | |
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11 | 18 | |
2,731 | 45 | |
0.4% | - | |
8.0 | 4.7 | |
about 1 month ago | almost 2 years ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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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.
rlalgorithms-tf2
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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.
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My implementations of RL algorithms + demo and tutorial
Hello deep learners, I added a tutorial jupyter notebook, which walks you through the features quickly and easily. I posted here about my project earlier for those who haven't seen it before, it has my reusable implementations of reinforcement learning algorithms available from the command line. I also added the project to pypi, which makes it available through pip install xagents
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RL command line tool demo + notebook
I created a command line tool for training and tuning and re-using reinforcement learning algorithms. For more info, you can check the project, and if you like you may also try the notebook I just added which walks you through how to use the features simply and quickly.
- Xagents: Deep reinforcement learning command line tool box
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xagents: deep reinforcement learning command line tool box
Project page
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Reinforcement learning quick start using OpenAI gym + xagents + Google Colab
xagents: python library based on tensorflow, which I developed, and it provides a command line interface for training and tuning algorithms on various environments.
- Xagents: Deep reinforcement learning Python library
- Autonomous learning command line tool box
- Elegant command line autonomous learning utility in Python
What are some alternatives?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
IRL - Algorithms for Inverse Reinforcement Learning
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
VMAgent - Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.
TensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Gymnasium - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
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]
DRL-robot-navigation - Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.