agents
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning. (by tensorflow)
pbt
Experiments with DeepMind's Population Based Training (by cipher813)
| agents | pbt | |
|---|---|---|
| 11 | 1 | |
| 2,731 | 0 | |
| 0.4% | - | |
| 8.0 | 10.0 | |
| about 1 month ago | over 5 years ago | |
| Python | ||
| Apache License 2.0 | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
agents
Posts with mentions or reviews of agents.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-03.
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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.
pbt
Posts with mentions or reviews of pbt.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-03.
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DD-PPO, TD3, SAC: which is the best?
Code for https://arxiv.org/abs/1711.09846 found: https://github.com/cipher813/pbt
What are some alternatives?
When comparing agents and pbt you can also consider the following projects:
gym - A toolkit for developing and comparing reinforcement learning algorithms.
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]
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
VMAgent - Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.
Gymnasium - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
GPflowOpt - Bayesian Optimization using GPflow
garage - A toolkit for reproducible reinforcement learning research.
rlalgorithms-tf2 - Packaged deep reinforcement learning algorithms in tensorflow 2.x