ReinforcementLearning | Reinforcing Your Learning of Reinforcement | Reinforcement Learning library
kandi X-RAY | ReinforcementLearning Summary
kandi X-RAY | ReinforcementLearning Summary
Reinforcing Your Learning of Reinforcement Learning
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Top functions reviewed by kandi - BETA
- Train an environment
- Print the state
- Preprocess a frame
- Create a game
- Stack a frame of frames
- Play a game
- Returns the winner of the player
- Checks if the player is in the player
- Move the board
- Build the model
- Resolve a residual layer
- Apply policy head layer
- Convolution layer
- Create a test environment
- Sample a batch of observations
- Play the game
- Test environment
- Return a random action
- Set the reward function
- Compute the logarithm of the model
- Prints the current state
- Record loss and value head loss
ReinforcementLearning Key Features
ReinforcementLearning Examples and Code Snippets
Community Discussions
Trending Discussions on ReinforcementLearning
QUESTION
My goal is to train an agent (ship) that takes two actions for now. 1. Choosing it's heading angle (where to go next) and 2. Choosing it's acceleration (if it will change its speed or not).
However, it seems like that I cannot undestand how to properly construct my action space and state space. I keep getting an error which I do not know how to fix. I have been trying to make it work using the Space wrapper.
I use the following code.
...ANSWER
Answered 2022-Apr-17 at 15:05I think the error message already explained it clearly.
QUESTION
I am trying to create a simultaneous multi agent environment using reinforcementlearning.jl
I have successfully represented the environment and it works with a RandomPolicy for every agent.
But my state space is large (actually it's a 14 tuple with each value in a certain range). So I can not use Tabular Approximators to estimate the Q or V values. That's why I have decided to use a Neural Network Approximator. But the docs do not discuss much about it, nor are there any examples were neural network approximator is used. I am stuck how to figure out how to use such approximator. If anyone can explain how to go about it, or refer to any example, it would be helpful.
Moreover I found from docs that using a Neural Network approximator needs us to use a CircularArraySARTTrajectory. But defining this trajectory requires a key word argument called capacity. I don't know what it means, nor it is discussed about in the docs and GitHub.
I tried writing the code that uses neural network approximator but I get error.
...ANSWER
Answered 2021-May-14 at 07:41Here the capacity
means the maximum length of the experience replay buffer. When applying DQN related algorithms, we usually use a circular buffer to store transitions at each step.
The error you posted above means that you forget to define the size of the state when defining the CircularArraySARTTrajectory
.
QUESTION
I have been learning Reinforcement Learning for few days now, and I have seen example problems like Mountain Car problem and Cart Pole problem.
In these problems, the way action space is described is discrete. For example in Cart Pole Problem, the agent can either move left or move right.
But the examples don't talk about how much? How does the agent decide how much to move left, how much to move right, after all these movements are continuous space actions. So I want to know how does the agent decide what real value to choose from a continuous action space.
Also I have been using ReinforcementLearning.jl in Julia and wanted to know a way i could represent range constraints on action space in it. Example, the real value that the agent chooses as it's action should lie in a range like [10.00, 20.00[ for example. I want to know how this can be done.
ANSWER
Answered 2021-May-11 at 07:27
- But the examples don't talk about how much? How does the agent decide how much to move left, how much to move right, after all these movements are continuous space actions. So I want to know how does the agent decide what real value to choose from a continuous action space.
The common solution is to assume that the output of the agent follows the normal distribution. Then you only need to design an agent that predicts the mean and std. Finally sample a random action from that distribution and pass it to the environment.
Another possible solution is to discretize the continuous action space and turn it into a discrete action space problem. Then randomly sample one action from the predicted bin.
- Also I have been using ReinforcementLearning.jl in Julia and wanted to know a way i could represent range constraints on action space in it. Example, the real value that the agent chooses as it's action should lie in a range like [10.00, 20.00[ for example. I want to know how this can be done.
You can take a look at the implementation detail of the PendulumEnv. Currently, it uses ..
from IntervalSets.jl to describe a continuous range.
QUESTION
I am following this tutorial on Policy Gradient using Keras, and can't quite figure out the below.
In the below case, how exactly are input tensors with different shapes fed to the model?
Layers are neither .concat
ed or .Add
ed.
input1.shape = (4, 4)
input2.shape = (4,)
- "input" layer has 4 neurons, and accepts
input1
+input2
as 4d vector??
The code excerpt (modified to make it simpler) :
...ANSWER
Answered 2021-Feb-22 at 11:44In cases where you might want to figure out what type of graph you have just build, it is helpful to use the model.summary()
or tf.keras.utils.plot_model()
methods for debugging:
QUESTION
I am working with Cython to speed up some python code and I am running into the following error:
...ANSWER
Answered 2020-Nov-22 at 13:01The problem lies in this part of your code:
QUESTION
I am following this online tutorial for coding a DQN,https://github.com/philtabor/Youtube-Code-Repository/blob/master/ReinforcementLearning/DeepQLearning/torch_deep_q_model.py , however I am running into this Runtime Error that I am unsure of how to debug or modify to prevent this error. Thanks!
...ANSWER
Answered 2020-Jun-06 at 09:40You have to do use .detach()
for :
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Install ReinforcementLearning
You can use ReinforcementLearning 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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