I have implemented a custom openai gym environment for a game similar to http://curvefever.io/, but with discreet actions instead of continuous. So my agent can in each step go in one of four directions, left/up/right/down. However one of these actions will always lead to the agent crashing into itself, since it cant "reverse".
Currently I just let the agent take any move, and just let it die if it makes an invalid move, hoping that it will eventually learn to not take that action in that state. I have however read that one can set the probabilities for making an illegal move zero, and then sample an action. Is there any other way to tackle this problem?
You can try to solve this by 2 changes:
1: give current direction as an input and give reward of maybe +0.1 if it takes the move which does not make it crash, and give -0.7 if it make a backward move which directly make it crash.
2: If you are using neural network and Softmax function as activation function of last layer, multiply all outputs of neural network with a positive integer ( confidence ) before giving it to Softmax function. it can be in range of 0 to 100 as i have experience more than 100 will not affect much. more the integer is the more confidence the agent will have to take action for a given state.
If you are not using neural network or say, deep learning, I suggest you to learn concepts of deep learning as your environment of game seems complex and a neural network will give best results.
Note: It will take huge amount of time. so you have to wait enough to train the algorithm. i suggest you not to hurry and let it train. and i played the game, its really interesting :) my wishes to make AI for the game :)
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I am trying to train a DQN Agent to solve AI Gym's Cartpole-v0 environment. I have started with this person's implementation just to get some hands-on experience. What I noticed is that during training, after many episodes the agent finds the solution and is able to keep the pole upright for the maximum amount of timesteps. However, after further training, the policy looks like it becomes more stochastic and it can't keep the pole upright anymore and goes in and out of a good policy. I'm pretty confused by this why wouldn't further training and experience help the agent? At episodes my epsilon for random action becomes very low, so it should be operating on just making the next prediction. So why does it on some training episodes fail to keep the pole upright and on others it succeeds?
Here is a picture of my reward-episode curve during the training process of the above linked implementation.
This actually looks fairly normal to me, in fact I guessed your results were from CartPole before reading the whole question.
I have a few suggestions:
When you're plotting results, you should plot averages over a few random seeds. Not only is this generally good practice (it shows how sensitive your algo is to seeds), it'll smooth out your graphs and give you a better understanding of the "skill" of your agent. Don't forget, the environment and the policy are stochastic, so it's not completely crazy that your agent exhibits this type of behavior.
Assuming you're implementing e-greedy exploration, what's your epsilon value? Are you decaying it over time? The issue could also be that your agent is still exploring a lot even after it found a good policy.
Have you played around with hyperparameters, like learning rate, epsilon, network size, replay buffer size, etc? Those can also be the culprit.
I have a concern in understanding why a target network is necessary in DQN? I’m reading paper on “human-level control through deep reinforcement learning”
I understand Q-learning. Q-learning is value-based reinforcement learning algorithm that learns “optimal” probability distribution between state-action that will maximize it’s long term discounted reward over a sequence of timesteps.
The Q-learning is updated using the bellman equation, and a single step of the q-learning update is given by
Q(S, A) = Q(S, A) + $\alpha$[R_(t+1) + $\gamma$ (Q(s’,a;’) - Q(s,a)]
Where alpha and gamma are learning and discount factors.
I can understand that the reinforcement learning algorithm will become unstable and diverge.
The experience replay buffer is used so that we do not forget past experiences and to de-correlate datasets provided to learn the probability distribution.
This is where I fail.
Let me break the paragraph from the paper down here for discussion
The fact that small updates to $Q$ may significantly change the policy and therefore change the data distribution — understood this part. Changes to Q-network periodically may lead to unstability and changes in distribution. For example, if we always take a left turn or something like this.
and the correlations between the action-values (Q) and the target values r + $gamma$ (argmax(Q(s’,a’)) — This says that the reward + gamma * my prediction of the return given that I take what I think is the best action in the current state and follow my policy from then on.
We used an iterative update that adjusts the action-values (Q) towards target values that are only periodically updated, thereby reducing correlations with the target.
So, in summary a target network required because the network keeps changing at each timestep and the “target values” are being updated at each timestep?
But I do not understand how it is going to solve it?
So, in summary a target network required because the network keeps changing at each timestep and the “target values” are being updated at each timestep?
The difference between Q-learning and DQN is that you have replaced an exact value function with a function approximator. With Q-learning you are updating exactly one state/action value at each timestep, whereas with DQN you are updating many, which you understand. The problem this causes is that you can affect the action values for the very next state you will be in instead of guaranteeing them to be stable as they are in Q-learning.
This happens basically all the time with DQN when using a standard deep network (bunch of layers of the same size fully connected). The effect you typically see with this is referred to as "catastrophic forgetting" and it can be quite spectacular. If you are doing something like moon lander with this sort of network (the simple one, not the pixel one) and track the rolling average score over the last 100 games or so, you will likely see a nice curve up in score, then all of a sudden it completely craps out starts making awful decisions again even as your alpha gets small. This cycle will continue endlessly regardless of how long you let it run.
Using a stable target network as your error measure is one way of combating this effect. Conceptually it's like saying, "I have an idea of how to play this well, I'm going to try it out for a bit until I find something better" as opposed to saying "I'm going to retrain myself how to play this entire game after every move". By giving your network more time to consider many actions that have taken place recently instead of updating all the time, it hopefully finds a more robust model before you start using it to make actions.
On a side note, DQN is essentially obsolete at this point, but the themes from that paper were the fuse leading up to the RL explosion of the last few years.
Call for experts in deep learning.
Hey, I am recently working on training images using tensorflow in python for tone mapping. To get the better result, I focused on using perceptual loss introduced from this paper by Justin Johnson.
In my implementation, I made the use of all 3 parts of loss: a feature loss that extracted from vgg16; a L2 pixel-level loss from the transferred image and the ground true image; and the total variation loss. I summed them up as the loss for back propagation.
From the function
yˆ=argminλcloss_content(y,yc)+λsloss_style(y,ys)+λTVloss_TV(y)
in the paper, we can see that there are 3 weights of the losses, the λ's, to balance them. The value of three λs are probably fixed throughout the training.
My question is that does it make sense if I dynamically change the λ's in every epoch(or several epochs) to adjust the importance of these losses?
For instance, the perceptual loss converges drastically in the first several epochs yet the pixel-level l2 loss converges fairly slow. So maybe the weight λs should be higher for the content loss, let's say 0.9, but lower for others. As the time passes, the pixel-level loss will be increasingly important to smooth up the image and to minimize the artifacts. So it might be better to adjust it higher a bit. Just like changing the learning rate according to the different epochs.
The postdoc supervises me straightly opposes my idea. He thought it is dynamically changing the training model and could cause the inconsistency of the training.
So, pro and cons, I need some ideas...
Thanks!
It's hard to answer this without knowing more about the data you're using, but in short, dynamic loss should not really have that much effect and may have opposite effect altogether.
If you are using Keras, you could simply run a hyperparameter tuner similar to the following in order to see if there is any effect (change the loss accordingly):
https://towardsdatascience.com/hyperparameter-optimization-with-keras-b82e6364ca53
I've only done this on smaller models (way too time consuming) but in essence, it's best to keep it constant and also avoid angering off your supervisor too :D
If you are running a different ML or DL library, there are optimizer for each, just Google them. It may be best to run these on a cluster and overnight, but they usually give you a good enough optimized version of your model.
Hope that helps and good luck!
So i wanna learn reinforcement learning by doing some examples. I wrote 2048 game but i do not know if i'm training it right. So as I understand I have to create neural network. I have created 16 inputs for each number. Then hidden layers 12x8 and 4 outputs for moves(up, right, down, left). (Activation function linear function for lat layer and relu for rest) Then I run one full game and save all the moves and rewards(0-nothing happend, -2-to moves that do nothink, -1 when that move lost game and a number of earned score when move do somethink). When the game ends I did backpropagation algorithm from the last move. Am i doing it rigth or what? And I know there are libraries like tensorflow but I wanna understand it all.
I would consult this GitHub repo, as it accomplishes exactly what you are trying to do.
You can actually use the above solution live here.
If you want to actually learn the fundamentals of how that all works, that's beyond the scope of what a single post on StackOverflow can provide.
I don't have much experience in machine learning, pattern recognition, data mining, etc. and in their underlying theory and systems.
I would like to develop an artificial model of the time it takes a human to make a move in a given Sudoku puzzle.
So what I'm looking for as an output from the machine learning process is a model that can give predictions on how long does it take for a target human to make a move in a given Sudoku situation.
Same input doesn't always map to same outcome. It takes different times for the human to make a move with the same situation, but my hypothesis is that there's a tendency in the resulting probability distribution. (My educated guess is that it is ~normal.)
I have ideas about the factors that influence the distribution (like #empty slots) but would preferably leave it to the system to figure these patterns out. Please notice, that I'm not interested in the patterns, just the model.
I can generate sample and test data easily by running sudoku puzzles and measuring the times it takes to make the moves.
What kind of learning algorithm would you suggest to use for this?
I was thinking NNs, but I'm not sure if they can have the desired property of giving weighted random outcomes for the same input.
If I understand this correctly you have an input vector of length 81, which contains 1 if the square is filled in and 0 otherwise. You want to learn a function which returns a probability distribution which models the response time of a human to that board position.
My first response would be that this is a regression problem and you should try straightforward linear regression. This will not provide you with a distribution of response times, but a single 'best-guess' response time.
I'm not clear on why you want to model a distribution of response times. However, if you really want to do want to output a distribution then it sounds like you want to look at Bayesian methods. I'm not really an expert on Bayesian inference, so I can't help you much further here.
However, I don't really think your approach is going to work because I agree with your intuition about features such as the number of empty slots being important. There are also other obvious features, such as the number of empty slots per row/column that are likely to be important. Explicitly putting these features in your representation will probably be much more successful than expecting that the learning algorithm will infer something similar on its own.
The monte carlo method seems like it would work well here but would require a stack of solutions the size of the moon to really do it. And it wouldn't give you the time per person, just the time on average.
My understanding of it, tenuous as it is, is that you have a database with a board position and the time it took a human to make the next move. At the very least you have a starting point for most moves. Even if it's not in the database you could start to calculate how long it would take to make a move based on some algorithm. Though I know you had specified you wanted machine learning to do this it might be worth segmenting the problem into something a little smaller then building on it.
If you have some guesstimate as to what influences the function (# of empty cell, etc), try to train a classifier on a vector of features, and not on the 81 cells vector (0/1 or 0..9, doesn't really matter for my argument).
I think that your claim:
we wouldn't have to necessary know the underlying patterns, the "trained patterns" in a learning system automatically encodes these sometimes quite delicate and subtle patterns inside them -- that's one of their great power
is wrong. you do have to give the network the right domain. for example, when trying to detect object in an image, working in the pixel domain is pointless. you'll only get results if you first run some feature detection to detect edges, corners, etc.
Theoretically, with enough non-linearity (in NN - enough layers in the network) it can detect such things, but in practice, I have never seen that work, without giving the classifier the right features to work with.
I was thinking NNs, but I'm not sure if they can have the desired property of giving weighted random outcomes for the same input.
You're just trying to learn a function from 2^81 or 10^81 (or a much smaller feature space as I suggest) to R (response time between 0 and Inf) or some discretization of that. So NN and other classifiers can do that.