dqn | Deep Q Learning Library | Machine Learning library
kandi X-RAY | dqn Summary
kandi X-RAY | dqn Summary
Deep Q Learning Library
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Play the agent
- Actual action
- Start a new episode
- Store an observation
- Append data to buffer
- Returns the action given the function
- Calculate the epsilon for a given frame
- Sample from num_plays
- Sample from the buffer
- Wrap a deep forest
- Get a random number of plays
- Create a basic environment
dqn Key Features
dqn Examples and Code Snippets
Lines of Code : 41
License : No License
Copydef main():
env = gym.make('CartPole-v0')
gamma = 0.99
copy_period = 50
D = len(env.observation_space.sample())
K = env.action_space.n
sizes = [200,200]
model = DQN(D, K, sizes, gamma)
tmodel = DQN(D, K, sizes, gamma)
init = tf.glo Community Discussions
Trending Discussions on dqn
QUESTION
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:
...ANSWER
Answered 2022-Apr-17 at 15:08Check 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 readt.reward, than looking what every index doing in steptclass DQNhas methodupdate, it's better to keep optimizer as attribute of class, than create it every time when calling functionbackprbgtusage of
torch.autograd.Variablehere is unnecessary, so it's also was taken awayupdate in
backprbgttaken per batchdecrease 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.
QUESTION
I want to compile my DQN Agent but I get error:
AttributeError: 'Adam' object has no attribute '_name',
ANSWER
Answered 2022-Apr-16 at 15:05Your 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.)
QUESTION
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
...ANSWER
Answered 2022-Mar-25 at 02:47loss does not really matter in RL. Very high loss is actually normal. In RL we care the reward most.
QUESTION
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:
...ANSWER
Answered 2022-Mar-22 at 23:02It 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.
QUESTION
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:
...ANSWER
Answered 2021-Dec-23 at 11:19As 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
QUESTION
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:
ANSWER
Answered 2022-Feb-01 at 15:46Inside 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 !
QUESTION
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.
...ANSWER
Answered 2022-Feb-01 at 07:31Issue is with input_shape. input_shape=input_shape[1:]
Working sample code
QUESTION
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
...ANSWER
Answered 2022-Jan-31 at 17:54I 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.
QUESTION
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:
ANSWER
Answered 2022-Jan-08 at 23:25The 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
QUESTION
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
...ANSWER
Answered 2021-Dec-19 at 16:09There 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.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install dqn
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.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
