tensorflow-rl | deep RL papers and random experimentation | Reinforcement Learning library

by steveKapturowski Python Version: Current License: Apache-2.0

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

kandi X-RAY | tensorflow-rl Summary

tensorflow-rl is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Pytorch, Tensorflow applications. tensorflow-rl has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However tensorflow-rl has 1 bugs. You can download it from GitHub.

Implementations of deep RL papers and random experimentation

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tensorflow-rl has a low active ecosystem.

It has 176 star(s) with 45 fork(s). There are 27 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 10 have been closed. On average issues are closed in 87 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-rl is current.

Quality

tensorflow-rl has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 102 code smells.

Security

tensorflow-rl has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-rl code analysis shows 0 unresolved vulnerabilities.

There are 7 security hotspots that need review.

License

tensorflow-rl is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

tensorflow-rl 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.

Installation instructions are not available. Examples and code snippets are available.

tensorflow-rl saves you 1562 person hours of effort in developing the same functionality from scratch.

It has 3476 lines of code, 254 functions and 39 files.

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

Top functions reviewed by kandi - BETA

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

Train the agent
Save checkpoint variables
Syncs the network with shared memory
Rescale reward
Train the actor
Compute targets according to the model
Calculate gradients
Calculate bootstrap value for given state
Build the q head
Return the log probability of a symbol
Generate the next action
Build q head
Run the optimizer
Build the policy head
Launch a cluster
Process the next action
Look up the q value for the given key
Calculate softmax and log - softmax
Perform doubledqn op
Build the encoder
Get the configuration
Train the model
Sample from the distribution
Train the network
Get the next action
Build qubits

Get all kandi verified functions for this library.

tensorflow-rl Key Features

No Key Features are available at this moment for tensorflow-rl.

tensorflow-rl Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow-rl.

Community Discussions

Trending Discussions on tensorflow-rl

Negative reward in reinforcement learning

What is the use of tf.select

QUESTION

Negative reward in reinforcement learning

Asked 2019-Feb-19 at 12:43

I can't wrap my head around question: how exactly negative rewards helps machine to avoid them?

Origin of the question came from google's solution for game Pong. By their logic, once game finished (agent won or lost point), environment returns reward (+1 or -1). Any intermediate states return 0 as reward. That means each win/loose will return either [0,0,0,...,0,1] either [0,0,0,...,0,-1] reward arrays. Then they discount and standardize rewards:

...

ANSWER

Answered 2019-Feb-19 at 11:42

"Tensorflow optimizer minimize loss by absolute value (doesn't care about sign, perfect loss is always 0). Right?"

Wrong. Minimizing the loss means trying to achieve as small a value as possible. That is, -100 is "better" than 0. Accordingly, -7.2 is better than 7.2. Thus, a value of 0 really carries no special significance, besides the fact that many loss functions are set up such that 0 determines the "optimal" value. However, these loss functions are usually set up to be non-negative, so the question of positive vs. negative values doesn't arise. Examples are cross entropy, squared error etc.

Source https://stackoverflow.com/questions/54759181

QUESTION

What is the use of tf.select

Asked 2017-Jan-18 at 20:40

(edited w.r.t. @quirk's answer)

I was reading some tensorflow-code online and saw this statements:

...

ANSWER

Answered 2017-Jan-06 at 14:56

Umm, RTD(Read the docs)!

tf.select selects elements from positive or negative tensors based on the boolness of the elements in the condition tensor.

tf.select(condition, t, e, name=None)
Selects elements from t or e, depending on condition.
The t, and e tensors must all have the same shape, and the output will also have that shape.

(from the official docs.)

So in your case:

threshold = tf.select(input > RLSA_THRESHOLD, positive, negative)

input > RLSA_THRESHOLD will be a tensor of bool or logical values (0 or 1 symbolically), which will help choose a value from either the positive vector or the negative vector.

For example, say you have a RLSA_THRESHOLD of 0.5 and your input vector is a 4-dimensional vector of real continuous values ranging from 0 to 1. Your positive and negative vectors are essentially [1, 1, 1, 1] and [0, 0, 0, 0], respectively. input is [0.8, 0.2, 0.5, 0.6].

threshold will be [1, 0, 0, 1].

NOTE: positive and negative could be any kind of tensor as long as the dimensions agree with the condition tensor. Had positive and negative been, say, [2, 4, 6, 8] and [1, 3, 5, 7] respectively, your threshold would have been [2, 3, 5, 8].

The code snippet seems reasonably advanced for me to assume that the authors would have just used input > RLSA_THRESHOLD if there was no specific reason for the tf.select.

There is a very good reason for that. input > RLSA_THRESHOLD would simply return a tensor of logical (boolean) values. Logical values do not mix well with numerical values. You cannot use them for any realistic numerical computation. Had the positive and/or negative tensors been real valued, you might have required your threshold tensor to also have real values, in case you planned to use them further along.

Is the tf.select equivalent to input > RLSA_THRESHOLD? If not, why not?

No they are not. One is a function, the other is a tensor.

I am going to give you the benefit of doubt and assume you meant to ask:

Is the threshold equivalent to input > RLSA_THRESHOLD? If not, why not?

No they are not. As explained above, input > RLSA_THRESHOLD is a logical tensor with a data type of bool. threshold, on the other hand, is a tensor with the same data type as positive and negative.

NOTE: You can always cast your logical tensors to numerical (or any other supported data type) tensors using any of the casting methods available in tensorflow.

Source https://stackoverflow.com/questions/41505746

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-rl

You can download it from GitHub.
You can use tensorflow-rl 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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