Reinforcement-Learning | 🤖 Implements of Reinforcement Learning algorithms | Reinforcement Learning library
kandi X-RAY | Reinforcement-Learning Summary
kandi X-RAY | Reinforcement-Learning Summary
This repo is implements of Reinforcement Learning Algorithms, implementing as learning, some of them are even another version of some tutorial. Any contributions are welcomed.
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Top functions reviewed by kandi - BETA
- Run the policy gradient
- Train the model
- Reset the observation
- Save a transition
- Train the environment
- Update Q table
- Checks if the given state exists in the q table
- Return the next action in the given state
- Run the optimizer
- Update Q - target net
- Sample from the buffer
- Run an actor thread
- Move the agent
- Get next action
- Sample a batch of data
- Returns the leaf index for a leaf node
- Update batch with q_value_diff
- Update p_value with p_value
- Add a transition to sum
- Adds p_value to t_data
- Calculate the next action
- Plot training steps
Reinforcement-Learning Key Features
Reinforcement-Learning Examples and Code Snippets
Lines of Code : 92License : Non-SPDX (Apache License 2.0)
Copydef 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'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:
QUESTION
I'making a implementation of Q-learning, specifically the Bellman equation.
I'm using the version from a website that guides he through the problem, but I have question: For maxQ, do I calculate the max reward using all Q-table values of the new state (s') - in my case 4 possible action (a'), each with their respective value- or the sum of the Q-table values of all the positions when taking the action (a')?
In other words, do I use the highest Q-value of all the possible actions I can take, or the summed Q-values of all the "neighbouring" squares?
...ANSWER
Answered 2019-Oct-20 at 14:37You always use max Q-value for all the possible actions you can take.
The idea is to pick the action with biggest (best) Q-Value of next state in order to stay in optimal policy Qpi* .
QUESTION
I'm trying to train a deep Q-learning Keras model to play CartPole-v1. However, it doesn't seem to get any better. I don't believe it's a bug but rather my lack of knowledge on how to use Keras and OpenAI Gym properly. I am following this tutorial (https://adventuresinmachinelearning.com/reinforcement-learning-tutorial-python-keras/), which shows how to train a bot to play NChain-v0 (which I was able to follow), but now I am trying to apply what I learned to a more complex environment: CartPole-v1. Here is the code below:
...ANSWER
Answered 2019-Jun-29 at 11:20Reinforcement learning is very noisy and your batch size is 1 which makes it even noisier. You can try to use a memory buffer of past episodes/updates which you update. You could use something like deque() from collections for this buffer. Then you randomly sample from this memory buffer according to a given batch-size. I found this repo to be very helpful (it includes a replay/memory buffer and a RL agent as you need it) https://github.com/udacity/deep-reinforcement-learning/tree/master/dqn Nevertheless, RL takes a long time to converge, unlike conventional deep learning where the loss decreases very fast in the beginning, in RL the reward will not increase for a long time and then suddenly start increasing.
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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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