keras-rl | Deep Reinforcement Learning for Keras
kandi X-RAY | keras-rl Summary
kandi X-RAY | keras-rl Summary
Deep Reinforcement Learning for Keras.
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of keras-rl
keras-rl Key Features
keras-rl Examples and Code Snippets
Lines of Code : 12License : Permissive (MIT)
Copyfrom helper.templates import Agent
class DoNothingAgent(Agent):
"""
An agent that chooses NOOP action at every timestep.
"""
def __init__(self, observation_space, action_space):
self.action = [0] * action_space.shape[0]
@misc{stelmaszczyk2017learning2run,
author = {Stelmaszczyk, Adam and Jarosik, Piotr},
title = "{Our NIPS 2017: Learning to Run source code}",
year = {2017},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = { @inproceedings{wang2020rlnoisy,
title={Reinforcement Learning with Perturbed Rewards},
author={Wang, Jingkang and Liu, Yang and Li, Bo},
booktitle={AAAI},
year={2020}
}
from __future__ import division
import argparse
from PIL import Image
import numpy as np
import gym
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Convolution2D, Permute
from keras.optimizers import Adam
im import numpy as np
import gym
from keras.models import Sequential, Model
from keras.layers import Dense, Activation, Flatten, Input, Concatenate
from keras.optimizers import Adam
from rl.agents import NAFAgent
from rl.memory import SequentialMemory import numpy as np
import gym
from gym import wrappers
from keras.models import Sequential, Model
from keras.layers import Dense, Activation, Flatten, Input, Concatenate
from keras.optimizers import Adam
from rl.processors import WhiteningNormaliz model = Model(inp, [a1,a2])
self.action_space = gym.spaces.Box(low=np.array([1]),high= np.array([3]), dtype=np.int)
actions= gym.spaces.Box(low=np.array([1]),high= np.array([3]), dtype=np.int)
for i in range(10):
print(actions.sample())
opt = tf.train.experimental.enable_mixed_precision_graph_rewrite(opt)
os.environ[‘TF_ENABLE_AUTO_MIXED_PRECISION’] = ‘1’
from time import sleep
sleep(0.0416) (24 fps)
env.step(action)
Community Discussions
Trending Discussions on keras-rl
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
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 training a DDPG agent on my custom environment that I wrote using openai gym. I am getting error during training the model.
When I search for a solution on web, I found that some people who faced similar issue were able to resolve it by initializing the variable.
...ANSWER
Answered 2021-Jun-10 at 07:00For now I was able to solve this error by replacing the imports from keras with imports from tensorflow.keras, although I don't know why keras itseld doesn't work
QUESTION
Sorry if this is a 'nooby' question, but I really don't know how to solve it. I've installed keras and a lot of other stuff for deep learning with Ananconda, but now I want to try to make something with Reinforcement Learning. So I've read that I need to install keras-rl, and I installed it as follows:
...ANSWER
Answered 2020-May-03 at 12:49Try installing it from the Conda command line, probably the environments don't match for Anaconda to realize that rl is a library
QUESTION
I am trying to implement a DQL model on one game of openAI gym. But it's giving me following error.
TypeError: len is not well defined for symbolic Tensors. (activation_3/Identity:0) Please call
x.shaperather thanlen(x)for shape information.
Creating a gym environment:
...ANSWER
Answered 2020-Jan-15 at 04:27The reason this breaks is because, tf.Tensor TF 2.0.0 (and TF 1.15) has the __len__ overloaded and raises an exception. But TF 1.14 for example doesn't have the __len__ attribute.
Therefore, anything TF 1.15+ (inclusive) breaks keras-rl (specifically here), which gives you the above error. So you got two options,
- Downgrade to TF 1.14 (recommended)
- Delete the
__len__overloading in TensorFlow source (not recommended as this can break other things)
QUESTION
I am trying to implement a DQN agent using Keras-rl. The problem is that when I define my model I need to use an LSTM layer in the architecture:
...ANSWER
Answered 2020-Jan-22 at 16:13The keras-rl library does not have explicit support for TensorFlow 2.0, so it will not work with such version of TensorFlow. The library is sparsely updated and the last release is around 2 years old (from 2018), so if you want to use it you should use TensorFlow 1.x
QUESTION
I have a custom environment in keras-rl with the following configurations in the constructor
...ANSWER
Answered 2020-Apr-12 at 07:52I am not sure why self.action_space = spaces.Discrete(3) is giving you actions as 0,2,4 since I cannot reproduce your error with the code snippet you posted, so I would suggest the following for defining your action
QUESTION
Trying to use gym open-ai package (and somen other) I ran into some problems, which structure I don't really understand.
As an example:
I tried to install gym in three different conda environments.
One way to do this is pip install gym Another is: git clone https://github.com/openai/gym.git cd gym pip install -e .
A third would be: pip3 install gym In some environments I would use Python2, in other env. maybe Python 3.7
Even more possibilities for installation would be:
sudo pip install gym
(and even more permutations would be possible, if we would take into account, if we activate an environment or don't activate any environment). To me things get even more complicated, because I tried to install conda with a not-administrator-user-account in Ubuntu, so that conda (or rather the user itself could not install any files in the /usr directory). I began to test some of this possibilities and cases, because installation of some libaries (e.g. keras-rl) seemed to need access to common ressources (/usr/ dir.), even if installed in an local conda environment. But if so: would the installations in different conda-environments interact? And what, if one would install a package as local user in a conda environment and afterward install a pip or pip3 as administrator. Would the admin-installation overwrite (or overrule or interact) the environmental installation (or parts of it)?
While experimenting with the different possibilities (or more: while trying to find a installations, which did not produce any errors like "gym not found" or "attribute error ... " ) there did occur errors like:
...ANSWER
Answered 2020-Jan-10 at 23:52you should not use sudo to install something in a conda environment. Most likely the used pip command is not stemming from the actual (activated?) environment, but the actual system-wide pip is used. Therefore you would need to use to use sudo to install to a system owned prefix.
You can check whether you are using the desired pip by invoking "which pip". The path should point to your environment. If it does not, you shall install pip inside your conda env.
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