kandi X-RAY | reinforcement-learning Summary
kandi X-RAY | reinforcement-learning Summary
This repository provides code, exercises and solutions for popular Reinforcement Learning algorithms. These are meant to serve as a learning tool to complement the theoretical materials from. Each folder in corresponds to one or more chapters of the above textbook and/or course. In addition to exercises and solution, each folder also contains a list of learning goals, a brief concept summary, and links to the relevant readings. All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.
Top functions reviewed by kandi - BETA
reinforcement-learning Key Features
reinforcement-learning Examples and Code Snippets
def noisy_linear_cosine_decay(learning_rate, global_step, decay_steps, initial_variance=1.0, variance_decay=0.55,
def linear_cosine_decay(learning_rate, global_step, decay_steps, num_periods=0.5, alpha=0.0, beta=0.001, n
def swish(features, beta=1.0): # pylint: disable=g-doc-args """Computes the SiLU or Swish activation function: `x * sigmoid(beta * x)`. beta : Hyperparameter for Swish activation function. Default value 1.0. The SiLU activation function was
Trending Discussions on reinforcement-learning
I have written the simple code below to check how to import a module:...
ANSWERAnswered 2021-Jul-28 at 18:06
Python automatically "binds" the first argument of a method that was declared in a class, to the instance of an object it was called on. So you have to instantiate your Test class (i.e., create a new
Test) in order to get this behavior. You can also just call it directly, but in this case, you have to provide the value of
self yourself (heh).
I'm learning DRL with the book Deep Reinforcement Learning in Action. In chapter 3, they present the simple game Gridworld (instructions here, in the rules section) with the corresponding code in PyTorch.
I've experimented with the code and it takes less than 3 minutes to train the network with 89% of wins (won 89 of 100 games after training).
As an exercise, I have migrated the code to tensorflow. All the code is here.
The problem is that with my tensorflow port it takes near 2 hours to train the network with a win rate of 84%. Both versions are using the only CPU to train (I don't have GPU)
Training loss figures seem correct and also the rate of a win (we have to take into consideration that the game is random and can have impossible states). The problem is the performance of the overall process.
I'm doing something terribly wrong, but what?
The main differences are in the training loop, in torch is this:...
ANSWERAnswered 2021-May-13 at 12:42
TensorFlow has 2 execution modes: eager execution, and graph mode.
TensorFlow default behavior, since version 2, is to default to eager execution. Eager execution is great as it enables you to write code close to how you would write standard python. It's easier to write, and it's easier to debug. Unfortunately, it's really not as fast as graph mode.
So the idea is, once the function is prototyped in eager mode, to make TensorFlow execute it in graph mode. For that you can use
tf.function compiles a callable into a TensorFlow graph. Once the function is compiled into a graph, the performance gain is usually quite important. The recommended approach when developing in
TensorFlow is the following:
- Debug in eager mode, then decorate with
- Don't rely on Python side effects like object mutation or list appends.
tf.functionworks best with TensorFlow ops; NumPy and Python calls are converted to constants.
I would add: think about the critical parts of your program, and which ones should be converted first into graph mode. It's usually the parts where you call a model to get a result. It's where you will see the best improvements.
You can find more information in the following guides:
tf.functionto your code
So, there are at least two things you can change in your code to make it run quite faster:
- The first one is to not use
model.predicton a small amount of data. The function is made to work on a huge dataset or on a generator. (See this comment on Github). Instead, you should call the model directly, and for performance enhancement, you can wrap the call to the model in a
Model.predict is a top-level API designed for batch-predicting outside of any loops, with the fully-features of the Keras APIs.
- The second one is to make your training step a separate function, and to decorate that function with
So, I would declare the following things before your training loop:
I am trying to repeat what is shown in this tutorial: https://www.kaggle.com/alexisbcook/deep-reinforcement-learning
When I run this code:...
ANSWERAnswered 2020-Sep-23 at 23:08
I fixed the problem by specifying TensorFlow version:
I have a
.txt file that contains data like this :
ANSWERAnswered 2020-Aug-08 at 13:29
Using an A2C agent from this article, how to get numerical values of
entropy_loss when weights are being updated?
The model I'm using is double-headed, both heads share the same trunk. The policy head output shape is
[number of actions, batch size] and value head has a shape of
[1, batch_size]. Compiling this model returns a size incompatibility error, when these loss functions are given as metrics:
ANSWERAnswered 2020-Apr-29 at 08:41
I found the answer to my problem. In Keras, the
metrics built-in functionality provides an interface for measuring performance and losses of the model, be it a custom or standard one.
When compiling a model as follows:
Using this code:...
ANSWERAnswered 2020-Apr-16 at 07:47
Yes, states can be represented by anything you want, including vectors of arbitrary length. Note, however, that if you are using a tabular version of Q-learning (or SARSA as in this case), you must have a discrete set of states. Therefore, you need a way to map the representation of your state (for example, a vector of potentially continuous values) to a set of discrete states.
Expanding on the example you have given, imagine that you have three states represented by vectors:
I am trying to run the code given by sentdex mentioned in https://pythonprogramming.net/reinforcement-learning-self-driving-autonomous-cars-carla-python/.
Windows 10, Carla 0.9.5, Python 3.7.5, Tensorflow 1.14.0.
I am not using any GPU version of the tensorflow. I have made few changes in the imports of the code. When I am running this code I am getting Resource not found error. Also note that an instance of carla is already running in background at port 2000 as mentioned in the code. Till now I have played with the imports by changing the locations of them along with installing different versions of tensorflow.
Changes in import:-...
ANSWERAnswered 2020-Mar-01 at 15:21
After many days of research I found out that the resources were not getting initialized and hence it was saying var does not exist. So, I found few lines of codes that I pasted after graph.as_default() and before saving the model. These are the lines of codes:-
epsilon_greedy is defined as :
ANSWERAnswered 2020-Mar-14 at 12:18
Yes, you are right. Usually you define a dictionary containing a map between integers and every action your agent can make. You can see that in the function n_actions is used exactly to sample a random action index when you don't select the optimal one.
I'm learning about Distributional RL from 'Deep Reinforcement Learning Hands On' code. And there is a method in model class:...
ANSWERAnswered 2020-Jan-09 at 16:45
It will call the
__call__ method on the instance. See this demo:
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