reinforcement_learning | Implementation of selected reinforcement | Reinforcement Learning library

by yrlu Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | reinforcement_learning Summary

reinforcement_learning is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Tensorflow applications. reinforcement_learning has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Implementation of selected reinforcement learning algorithms in Tensorflow. A3C, DDPG, REINFORCE, DQN, etc.

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reinforcement_learning has a low active ecosystem.

It has 144 star(s) with 47 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 3 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of reinforcement_learning is current.

Quality

reinforcement_learning has 0 bugs and 0 code smells.

Security

reinforcement_learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

reinforcement_learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

reinforcement_learning is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

reinforcement_learning releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

reinforcement_learning saves you 875 person hours of effort in developing the same functionality from scratch.

It has 2002 lines of code, 161 functions and 40 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed reinforcement_learning and discovered the below as its top functions. This is intended to give you an instant insight into reinforcement_learning implemented functionality, and help decide if they suit your requirements.

Build the network
Compute the fc
Learn the reward function
Compute the value of the transition matrix
Add a key to the set
Return the value of a key
Train the model
Get the gradients for the given state
Get the QValueTarget for the given state and action
Get an action bound to the actor
Evaluate the policy distribution
Get abstract actions
Return the reward for a given state
Get abstract states
Learn a function
Build the policy net
Build the qnet
Return a random action
Connects the graph
Get the optimal policy
Get the action of a given state
Learn a single epoch
Calculate the optimal policy
Return the distribution distance between states
Return q values for given state and action
Compute the action noise

Get all kandi verified functions for this library.

reinforcement_learning Key Features

No Key Features are available at this moment for reinforcement_learning.

reinforcement_learning Examples and Code Snippets

No Code Snippets are available at this moment for reinforcement_learning.

Community Discussions

Trending Discussions on reinforcement_learning

ValueError: Tape is still recording, This can happen if you try to re-enter an already-active tape

Reinforcement Learning coach : Saver fails to restore agent's checkpoint

QUESTION

ValueError: Tape is still recording, This can happen if you try to re-enter an already-active tape

Asked 2021-Jan-15 at 12:05

I write some tensorflow code about Deep Successor Representation (DSQ) reinforcement learning:

...

ANSWER

Answered 2021-Jan-15 at 08:07

A call to the optimizer must be out of the scope of the gradient tape, i.e:

Source https://stackoverflow.com/questions/65732431

QUESTION

Reinforcement Learning coach : Saver fails to restore agent's checkpoint

Asked 2020-Oct-17 at 11:54

I'm using rl coach through AWS Sagemaker, and I'm running in an issue that I struggle to understand.

I'm performing RL using AWS Sagemaker for the learning, and AWS Robomaker for the environment, like in DeepRacer which uses rl coach as well. In fact, the code only little differs with the DeepRacer code on the learning side. But the environment is completely different though.

What happens:

The graph manager initialization succeeds
A first checkpoint is generated (and uploaded to S3)
The agent loads the first checkpoint
The agent performs N episodes with the first policy
The graph manager fetches the N episodes
The graph manager performs 1 training step and create a second checkpoint (uploaded to S3)
The agent fails to restore the model with the second checkpoint.

The agent raises an exception with the message : Failed to restore agent's checkpoint: 'main_level/agent/main/online/global_step'

The traceback points to a bug happening in this rl coach module:

...

ANSWER

Answered 2020-Oct-17 at 11:54

I removed the patch (technically I removed the patch command in my dockerfile that was applying it), and now it works, the model is correctly restored from the checkpoint.

Source https://stackoverflow.com/questions/64349126

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install reinforcement_learning

You can download it from GitHub.
You can use reinforcement_learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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