Qlearning | Applying Q-learning based algorithm | Reinforcement Learning library
kandi X-RAY | Qlearning Summary
kandi X-RAY | Qlearning Summary
Applying Q-learning based algorithm for various games and environments.
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Top functions reviewed by kandi - BETA
- Train the model
- Forward a single step
- Check if player crash in ground
- Defines the model
- Check if two rects collide with the given rect
- Generate random action
- Generate a random pipe
- Copy a network from net1 to net2
- Preprocess input image
- Load sprite images
- Gets the hitmask of an image
- Play the game
- Construct the net
- Generalized convolution layer
- Lrelu layer
- Reward a bandit
Qlearning Key Features
Qlearning Examples and Code Snippets
Community Discussions
Trending Discussions on Qlearning
QUESTION
i try to create a simplified rl4j example based on the existing Gym and Malmo examples. Given is a sine wave and the AI should say if we are on top of the wave, on bottom or somewhere else(noop).
The SineRider is the "Game", State is the value of the sine function(Just one double)
The problem is it never calls the step function in SineRider to get a reward. What do i wrong?
Kotlin:
...ANSWER
Answered 2020-Jun-16 at 11:23The problem was the isDone() function. It say always the game is over.
Code changes:
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
Is there any way of updating the probabilities within an existing instance of the class EnumeratedIntegerDistribution without creating an entirely new instance?
BackgroundI'm trying to implement a simplified Q-learning style demonstration using an android phone. I need to update the probabilities for each item with each loop through the learning process. Currently I am unable to find any method accessible from my instance of enumeratedIntegerDistribution that will let me reset|update|modify these probabilities. Therefore, the only way I can see to do this is to create a new instance of EnumeratedIntegerDistribution within each loop. Keeping in mind that each of these loops is only 20ms long, it is my understanding that this would be terribly memory inefficient compared to creating one instance and updating the values within the existing instance. Is there no standard set-style methods to update these probabilities? If not, is there a recommended workaround (i.e. using a different class, making my own class, overriding something to make it accessible, etc.?)
A follow up would be whether or not this question is a moot effort. Would the compiled code actually be any more/less efficient by trying to avoid this new instance every loop? (I'm not knowledgeable enough to know how compilers would handle such things).
CodeA minimal example below:
...ANSWER
Answered 2019-Nov-27 at 16:40Unfortunately it is not possible to update the existing EnumeratedIntegerDistribution. I have had similar issue in the past and I ended up re-creating the instance everytime I need to update the chances.
I won't worry too much about the memory allocations as those will be short-lived objects. These are micro-optimisations you should not worry about.
In my project I did implement a cleaner way with interfaces to create instances of these EnumeratedDistribution class.
This is not the direct answer but might guide you in the right direction.
QUESTION
I am working on learning q-tables and ran through a simple version which only used a 1-dimensional array to move forward and backward. now I am trying 4 direction movement and got stuck on controlling the person.
I got the random movement down now and it will eventually find the goal. but I want it to learn how to get to the goal instead of randomly stumbling on it. So I would appreciate any advice on adding a qlearning into this code. Thank you.
Here is my full code as it stupid simple right now.
...ANSWER
Answered 2019-Jun-24 at 08:07I have a few suggestions based on your code example:
separate the environment from the agent. The environment needs to have a method of the form
new_state, reward = env.step(old_state, action). This method is saying how an action transforms your old state into a new state. It's a good idea to encode your states and actions as simple integers. I strongly recommend setting up unit tests for this method.the agent then needs to have an equivalent method
action = agent.policy(state, reward). As a first pass, you should manually code an agent that does what you think is right. e.g., it might just try to head towards the goal location.consider the issue of whether the state representation is Markovian. If you could do better at the problem if you had a memory of all the past states you visited, then the state doesn't have the Markov property. Preferably, the state representation should be compact (the smallest set that is still Markovian).
once this structure is set-up, you can then think about actually learning a Q table. One possible method (that is easy to understand but not necessarily that efficient) is Monte Carlo with either exploring starts or epsilon-soft greedy. A good RL book should give pseudocode for either variant.
When you are feeling confident, head to openai gym https://gym.openai.com/ for some more detailed class structures. There are some hints about creating your own environments here: https://gym.openai.com/docs/#environments
QUESTION
I am interested in implementing Q-learning (or some form of reinforcement learning) to find an optimal protocol. Currently, I have a function written in Python where I can take in the protocol or "action" and "state" and returns a new state and a "reward". However, I am having trouble finding a Python implementation of Q-learning that I can use in this situation (i.e. something that can learn the function as if it is a black box). I have looked at OpenAI gym but that would require writing a new environment. Would anyone know of a simpler package or script that I can adopt for this?
My code is of the form:
...ANSWER
Answered 2018-Sep-10 at 17:13A lot of the comments presented here require you to have deep knowledge of reinforcement learning. It seems that you are just getting started with reinforcement learning, so I would recommend starting with the most basic Q learning algorithm.
The best way to learn RL is to code the basic algorithm for yourself. The algorithm has two parts (model, agent) and it looks like this:
QUESTION
I followed the instructions on how to setup jupyter on a AWS instance, but am unable to access it from my personal computer at the address https://127.0.0.1:8157 after setting up the ssh tunnel and starting the notebook. It just hangs. Anyone have ideas on how to fix.
I used the following security settings
I opened a tunnel as so
ssh -i keypair.pem -L 8157:127.0.0.1:8888 ubuntu@
and ran the instance as
$ jupyter notebook
...
[I 03:59:30.778 NotebookApp] Serving notebooks from local directory: /home/ubuntu/qlearning [I 03:59:30.778 NotebookApp] 0 active kernels [I 03:59:30.779 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/?token=4b35857968acb4a75dc4b7fdd246c20b967dcfaaa13799c2 [I 03:59:30.779 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). [W 03:59:30.779 NotebookApp] No web browser found: could not locate runnable browser. [C 03:59:30.779 NotebookApp]
ANSWER
Answered 2018-May-21 at 04:30You should use http://127.0.0.1:8157 not https://
*There's no need to open port 8888 in the security group, as you are tunneling the connection through port 22.
QUESTION
I have implemented VI (Value Iteration), PI (Policy Iteration), and QLearning algorithms using python. After comparing results, I noticed something. VI and PI algorithms converge to same utilities and policies. With same parameters, QLearning algorithm converge to different utilities, but same policies with VI and PI algorithms. Is this something normal? I read a lot of papers and books about MDP and RL, but couldn't find anything which tells if utilities of VI-PI algorithms should converge to same utilities with QLearning or not.
Following information is about my grid world and results.
MY GRID WORLD
- States => {s0, s1, ... , s10}
- Actions => {a0, a1, a2, a3} where: a0 = Up, a1 = Right, a2 = Down, a3 = Left for all states
- There are 4 terminal states, which have +1, +1, -10, +10 rewards.
- Initial state is s6
- Transition probability of an action is P, and (1 - p) / 2 to go left or right side of that action. (For example: If P = 0.8, when agent tries to go UP, with 80% chance agent will go UP, and with 10% chance agent will go RIGHT, and 10% LEFT.)
RESULTS
- VI and PI algorithm results with Reward = -0.02, Discount Factor = 0.8, Probability = 0.8
- VI converges after 50 iterations, PI converges after 3 iteration
- QLearning algorithm results with Reward = -0.02, Discount Factor = 0.8, Learning Rate = 0.1, Epsilon (For exploration) = 0.1
- Resulting utilities on the image of QLearning results are the maximum Q(s, a) pairs of each state.
qLearning_1million_10million_iterations_results.png
In addition, I also noticed that, when QLearning does 1 million iterations, states which are equally far away from the +10 rewarded terminal have the same utilities. Agent seems it does not care if it is going to the reward from a path which is near to -10 terminal or not, while agent cares about it on VI and PI algorithms. Is this because, in QLearning, we don't know the transition probability of environment?
...ANSWER
Answered 2017-Dec-29 at 09:23If the state and action spaces are finite, as in your problem, Q-learning algorithm should converge asymptotically to the optimal utility (aka, Q-function), when the number of transitions approaches to infinity and under the following conditions:
where n is the number of transitions and a is the learning rate.
This conditions requires updating your learning rate as learning progresses. A typical choice could be use a_n = 1/n. However, in practice, the learning rate schedule may require some tuning depending on the problem.
On the other hand, another convergence condition consists in update all state-action pairs infinitely (in a asymtotical sense). This could be achieved simply by maintaining an exploration rate bigger than zero.
So, in your case, you need to decrease the learning rate.
QUESTION
I want to create a line in the Tkinter and after changing the height I need to delete the previous line and create a new line with the new height, this is a repeated process.
To this, I read the tutorial of the Tkinter of python and I think the After method might be useful. So, I write my idea but it is not a good method and I cannot create it. Also, I searched about a Shown event in the Tkinter, but I did not find the Shown event in the Tkinter for the window.
Here is my suggested code:
...ANSWER
Answered 2017-Jul-05 at 22:38As Bryan mentioned, you can modify an item on your canvas using the itemconfig or coords methods (see Change the attributes of a tkinter canvas object)
Then, to create an animation loop with the after method, you need to let the animation function call itself multiple times.
Example:
QUESTION
I've read on wikipedia https://en.wikipedia.org/wiki/Q-learning
Q-learning may suffer from slow rate of convergence, especially when the discount factor {\displaystyle \gamma } \gamma is close to one.[16] Speedy Q-learning, a new variant of Q-learning algorithm, deals with this problem and achieves a slightly better rate of convergence than model-based methods such as value iteration
So I wanted to try speedy q-learning, and see how better it is.
The only source about it I could find on the internet is this: https://papers.nips.cc/paper/4251-speedy-q-learning.pdf
That's the algorithm they suggest.
Now, I didn't understand it. what excactly is TkQk, Am I supposed to have another list of q-values? Is there any clearer explanation than this?
...ANSWER
Answered 2017-Jan-18 at 09:06A first consideration: if you are trying to speed up Q-learning for a practical problem, I would choose other options before Speedy Q-learning, such as the well-known Q(lambda), i.e., Q-learning combined with elegibility traces. Why? Becuase there are tons of information and experimental (good) results with eligibility traces. In fact, as the Speedy Q-learning authors suggest, the working principle of both methods are similar:
The idea of using previous estimates of the action-values has already been used to improve the performance of Q-learning. A popular algorithm of this kind is
Q(lambda)[14, 20], which incorporates the concept of eligibility traces in Q-learning, and has been empirically shown to have a better performance than Q-learning, i.e., Q(0), for suitable values oflambda.
You can find a nice introduction in the Sutton and Barto RL book. If you are simply interested in study the differences between Speedy Q-learning and the standard version, go on.
A now your question. Yes, you have to maintain two separate lists of Q-values, one for the current time k and another for the previous k-1, namely, Q_{k} and Q_{k-1} respectively.
In the common case (including your case), TQ_{k} = r(x,a) + discountFactor * max_{b in A} Q_{k}(y,b), where y is the next state and b the action that maximizes Q_{k}for the given state. Notice that you are using that operator in the standard Q-learning, which has the following update rule:
In the case of Speedy Q-learning (SQL), as peviously stated, you maintain two Q-functions and apply the operation TQ to both: TQ_{k} and TQ_{k-1}. Then the results of the previous operations are used in the SQL update rule:
Another point to highlight in the pseudo-code you post in your question, it that it corresponds with the synchronous version of SQL. This means that, in each time step k, you need to generate the next state y and update Q_{k+1}(x,a) for all the existing state-actions pairs (x,a).
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Install Qlearning
You can use Qlearning 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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