agents
tensorforce
| agents | tensorforce | |
|---|---|---|
| 11 | 1 | |
| 2,731 | 3,281 | |
| 0.4% | 0.2% | |
| 8.0 | 3.0 | |
| about 1 month ago | 25 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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
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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.
tensorforce
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Advice on doing RL for Settlers of Catan?
The most promising approach has been using the TensorForce framework (https://github.com/tensorforce/tensorforce) with a custom environment that represents a simpler game (1v1 against a bot that chooses actions randomly, no trading between players, and fixing discarding to be done automatically and at random).
What are some alternatives?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
VMAgent - Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
Gymnasium - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
deepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving
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]
trax - Trax — Deep Learning with Clear Code and Speed
GPflowOpt - Bayesian Optimization using GPflow
action-branching-agents - (AAAI 2018) Action Branching Architectures for Deep Reinforcement Learning