reinforcement-learning | Minimal and Clean Reinforcement Learning Examples | Reinforcement Learning library
kandi X-RAY | reinforcement-learning Summary
kandi X-RAY | reinforcement-learning Summary
Minimal and clean examples of reinforcement learning algorithms presented by RLCode team. [한국어] Maintainers - Woongwon, Youngmoo, Hyeokreal, Uiryeong, Keon.
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Top functions reviewed by kandi - BETA
- Train the model
- Returns n random samples from the tree
- Retrieve an item from the queue
- Internal method to retrieve an index from the tree
- Move the circle
- Convert coordinates to state coordinates
- Check if the reward is in the given state
- Gets the state of the agent
- Convert i to np array
- Return the action given a state
- Return the action of the given state
- Resets the widget to the current state
- Print the optimal policy
- Calculate action
- Calculate the action given the history
- Learn the given action
- Calculates the value of all texts
- Calculate action based on q function
- Train the replay
- Gets the action list for the given state
- Move the rectangle by policy
- Train a replay
- Print the text value of the QTable
- Returns the action corresponding to the given state
- Add data to the accumulator
- Update the model
reinforcement-learning Key Features
reinforcement-learning Examples and Code Snippets
Lines of Code : 80License : Strong Copyleft (GPL-3.0)
Copyfreqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
if informative is None:
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
class MyCoolRLModel(ReinforcementLearner):
"""
INFO:mlagents_envs:
'Ball3DAcademy' started successfully!
Unity Academy name: Ball3DAcademy
INFO:mlagents_envs:Connected new brain:
Unity brain name: 3DBallLearning
Number of Visual Observations (per agent): 0
Vector Observation spac 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 Community Discussions
Trending Discussions on reinforcement-learning
QUESTION
I have written the simple code below to check how to import a module:
...ANSWER
Answered 2021-Jul-28 at 18:06Python 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).
QUESTION
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:
...ANSWER
Answered 2021-May-13 at 12:42TensorFlow 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. 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
@tf.function.- 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:
Applyingtf.function to 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 atf.function.
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
@tf.function.
So, I would declare the following things before your training loop:
QUESTION
I am trying to repeat what is shown in this tutorial: https://www.kaggle.com/alexisbcook/deep-reinforcement-learning
When I run this code:
...ANSWER
Answered 2020-Sep-23 at 23:08I fixed the problem by specifying TensorFlow version:
QUESTION
I have a .txt file that contains data like this :
ANSWER
Answered 2020-Aug-08 at 13:29Alternativey, use
QUESTION
Using an A2C agent from this article, how to get numerical values of value_loss, policy_loss and 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:
ANSWER
Answered 2020-Apr-29 at 08:41I 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:
QUESTION
Using this code:
...ANSWER
Answered 2020-Apr-16 at 07:47Yes, 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:
QUESTION
I am trying to run the code given by sentdex mentioned in https://pythonprogramming.net/reinforcement-learning-self-driving-autonomous-cars-carla-python/.
Specifications:-
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:-
...ANSWER
Answered 2020-Mar-01 at 15:21After 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:-
QUESTION
Reading https://towardsdatascience.com/reinforcement-learning-temporal-difference-sarsa-q-learning-expected-sarsa-on-python-9fecfda7467e epsilon_greedy is defined as :
ANSWER
Answered 2020-Mar-14 at 12:18Yes, 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.
QUESTION
I'm learning about Distributional RL from 'Deep Reinforcement Learning Hands On' code. And there is a method in model class:
...ANSWER
Answered 2020-Jan-09 at 16:45It will call the __call__ method on the instance. See this demo:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
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Install reinforcement-learning
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.
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