dqn | DQN implementation in PyTorch | Machine Learning library

 by   rlcode Python Version: Current License: No License

kandi X-RAY | dqn Summary

kandi X-RAY | dqn Summary

dqn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. dqn has no bugs, it has no vulnerabilities and it has low support. However dqn build file is not available. You can download it from GitHub.

DQN implementation in PyTorch.

            kandi-support Support

              dqn has a low active ecosystem.
              It has 9 star(s) with 4 fork(s). There are 6 watchers for this library.
              It had no major release in the last 6 months.
              dqn has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of dqn is current.

            kandi-Quality Quality

              dqn has 0 bugs and 0 code smells.

            kandi-Security Security

              dqn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              dqn code analysis shows 0 unresolved vulnerabilities.
              There are 1 security hotspots that need review.

            kandi-License License

              dqn does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              dqn releases are not available. You will need to build from source code and install.
              dqn has no build file. You will be need to create the build yourself to build the component from source.
              It has 131 lines of code, 8 functions and 1 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed dqn and discovered the below as its top functions. This is intended to give you an instant insight into dqn implemented functionality, and help decide if they suit your requirements.
            • Train the model .
            • Initialize the model .
            • Return the action corresponding to the given state .
            • Initializes weights .
            • Forward the forward function .
            • Update the target model .
            Get all kandi verified functions for this library.

            dqn Key Features

            No Key Features are available at this moment for dqn.

            dqn Examples and Code Snippets

            No Code Snippets are available at this moment for dqn.

            Community Discussions


            The DQN model cannot correctly come out the expected scores
            Asked 2022-Apr-17 at 15:08

            I am working on a DQN training model of the game "CartPole-v1". In this model, the system did not remind any error information in the terminal. However, The result evaluation got worse.This is the output data:



            Answered 2022-Apr-17 at 15:08

            Check out the code. For most parts it's the same as in snippet above, but there is some changes:

            • for step in replay buffer (which is called in code memory_store) namedtuple is used, and in update it's much easier to read t.reward, than looking what every index doing in step t

            • class DQN has method update, it's better to keep optimizer as attribute of class, than create it every time when calling function backprbgt

            • usage of torch.autograd.Variable here is unnecessary, so it's also was taken away

            • update in backprbgt taken per batch

            • decrease size of hidden layer from 360 to 32, while increase batch size from 40 to 128

            • updating network once in 10 episodes, but on 10 batches in replay buffer

            • average score prints out every 50 episodes based on 10 last episodes

            • add seeds

            Also for RL it's take a long time to learn anything, so hoping that after 100 episodes it'll be close to even 100 points is somewhat optimistic. For the code in link averaging on 5 runs results in following dynamics

            X axis -- number of episodes (yeah, 70 K, but it's like 20 minutes of real time)

            Y axis -- number of steps in episode

            As can be seen after 70K episodes algorithm achieves reward comparable to highest possible in this environment (highest -- 500). By tweaking hyperparameters faster rate can be achieved, but also remember it's DQN without any modification.

            Source https://stackoverflow.com/questions/71897010


            Keras: AttributeError: 'Adam' object has no attribute '_name'
            Asked 2022-Apr-16 at 15:05

            I want to compile my DQN Agent but I get error: AttributeError: 'Adam' object has no attribute '_name',



            Answered 2022-Apr-16 at 15:05

            Your error came from importing Adam with from keras.optimizer_v1 import Adam, You can solve your problem with tf.keras.optimizers.Adam from TensorFlow >= v2 like below:

            (The lr argument is deprecated, it's better to use learning_rate instead.)

            Source https://stackoverflow.com/questions/71894769


            Why does my model not learn? Very high loss
            Asked 2022-Mar-25 at 10:49

            I built a simulation model where trucks collect garbage containers based on their fill level. I used OpenAi Gym and Tensorflow/keras to create my Deep Reinforcement Learning model... But my training has a very high loss... Where did I go wrong? Thanks in advance

            this is the Env



            Answered 2022-Mar-25 at 02:47

            loss does not really matter in RL. Very high loss is actually normal. In RL we care the reward most.

            Source https://stackoverflow.com/questions/71575887


            Unable to allocate memory with array shape to create reinforcement learning model
            Asked 2022-Mar-22 at 23:02

            I am trying to create a DQN model for mario environment. But when I try to create the model it gives me this error:

            MemoryError: Unable to allocate 229. GiB for an array with shape (1000000, 1, 4, 240, 256) and data type uint8

            This is the code for creating the model:



            Answered 2022-Mar-22 at 23:02

            It looks like you simply don't have enough RAM to allocate 229 GiB for an array of that size---which is incredibly large---very few computers could.

            Have you tried batching your idea into batches of either 64, 128, 256, etc.? That is a very common way to decrease the memory load, and you can experiment with different values to see what computation you can handle. Tensorflow has a great deal of built-in methods that can help you here. One place to look would be the batch method here.

            Source https://stackoverflow.com/questions/71558407


            OpenAI-Gym and Keras-RL: DQN expects a model that has one dimension for each action
            Asked 2022-Mar-02 at 10:55

            I am trying to set a Deep-Q-Learning agent with a custom environment in OpenAI Gym. I have 4 continuous state variables with individual limits and 3 integer action variables with individual limits.

            Here is the code:



            Answered 2021-Dec-23 at 11:19

            As we talked about in the comments, it seems that the Keras-rl library is no longer supported (the last update in the repository was in 2019), so it's possible that everything is inside Keras now. I take a look at Keras documentation and there are no high-level functions to build a reinforcement learning model, but is possible to use lower-level functions to this.

            • Here is an example of how to use Deep Q-Learning with Keras: link

            Another solution may be to downgrade to Tensorflow 1.0 as it seems the compatibility problem occurs due to some changes in version 2.0. I didn't test, but maybe the Keras-rl + Tensorflow 1.0 may work.

            There is also a branch of Keras-rl to support Tensorflow 2.0, the repository is archived, but there is a chance that it will work for you

            Source https://stackoverflow.com/questions/70261352


            process value of an attribute of an object without changing it in python
            Asked 2022-Feb-02 at 11:01

            Hello I am struggling with a Problem in my Python code.

            My problem is the following: I want to copy the value of an attribute of an object. I create the object agent, then in the function train_agent() I copy the value of the attribute state_vec to the variable last_state_vec. The Problem now is, when I change last_state_vec I automatically also change the attribute state_vec of agent state_vec = np.array(0,0,10,0,0). How can I only copy the value of state_vec, so that it doesn't get changed in this case. I want state_vec to stay the zero vector. Here's some of my code:



            Answered 2022-Feb-01 at 15:46

            Inside train_agent() function you can set last_state_vec to agent.state_vec.copy().

            Currently you are initializing the variable last_state_vec with the reference of agent.state_vec.

            So By replacing it with agent.state_vec.copy() should do the trick !

            Source https://stackoverflow.com/questions/70943249


            ValueError: Input 0 of layer "max_pooling2d" is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: (None, 3, 51, 39, 32)
            Asked 2022-Feb-01 at 07:31

            I have two different problems occurs at the same time.

            I am having dimensionality problems with MaxPooling2d and having same dimensionality problem with DQNAgent.

            The thing is, I can fix them seperately but cannot at the same time.

            First Problem

            I am trying to build a CNN network with several layers. After I build my model, when I try to run it, it gives me an error.



            Answered 2022-Feb-01 at 07:31

            Issue is with input_shape. input_shape=input_shape[1:]

            Working sample code

            Source https://stackoverflow.com/questions/70808035


            Training DQN Agent with Multidiscrete action space in gym
            Asked 2022-Jan-31 at 17:54

            I would like to train a DQN Agent with Keras-rl. My environment has both multi-discrete action and observation spaces. I am adapting the code of this video: https://www.youtube.com/watch?v=bD6V3rcr_54&t=5s

            Then, I am sharing my code



            Answered 2022-Jan-31 at 17:54

            I had the same problem, unfortunately it's impossible to use gym.spaces.MultiDiscrete with the DQNAgent in Keras-rl.


            Use the library stable-baselines3 and use the A2C agent. It's very easy to implement it.

            Source https://stackoverflow.com/questions/70861260


            RuntimeError: Found dtype Double but expected Float - PyTorch
            Asked 2022-Jan-08 at 23:25

            I am new to pytorch and I am working on DQN for a timeseries using Reinforcement Learning and I needed to have a complex observation of timeseries and some sensor readings, so I merged two neural networks and I am not sure if that's what is ruining my loss.backward or something else. I know there is multiple questions with the same title but none worked for me, maybe I am missing something.
            First of all, this is my network:



            Answered 2022-Jan-08 at 23:25

            The issue wasn't in the input to the network but the criterion of the MSELoss, so it worked fine after casting the criterion to float as below

            Source https://stackoverflow.com/questions/70615514


            DQN predicts same action value for every state (cart pole)
            Asked 2021-Dec-22 at 15:55

            I'm trying to implement a DQN. As a warm up I want to solve CartPole-v0 with a MLP consisting of two hidden layers along with input and output layers. The input is a 4 element array [cart position, cart velocity, pole angle, pole angular velocity] and output is an action value for each action (left or right). I am not exactly implementing a DQN from the "Playing Atari with DRL" paper (no frame stacking for inputs etc). I also made a few non standard choices like putting done and the target network prediction of action value in the experience replay, but those choices shouldn't affect learning.

            In any case I'm having a lot of trouble getting the thing to work. No matter how long I train the agent it keeps predicting a higher value for one action over another, for example Q(s, Right)> Q(s, Left) for all states s. Below is my learning code, my network definition, and some results I get from training



            Answered 2021-Dec-19 at 16:09

            There was nothing wrong with the network definition. It turns out the learning rate was too high and reducing it 0.00025 (as in the original Nature paper introducing the DQN) led to an agent which can solve CartPole-v0.

            That said, the learning algorithm was incorrect. In particular I was using the wrong target action-value predictions. Note the algorithm laid out above does not use the most recent version of the target network to make predictions. This leads to poor results as training progresses because the agent is learning based on stale target data. The way to fix this is to just put (s, a, r, s', done) into the replay memory and then make target predictions using the most up to date version of the target network when sampling a mini batch. See the code below for an updated learning loop.

            Source https://stackoverflow.com/questions/70382999

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


            No vulnerabilities reported

            Install dqn

            You can download it from GitHub.
            You can use dqn 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.


            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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