Questions tagged [q-learning]

For questions related to the Q-learning algorithm, which is a model-free and temporal-difference reinforcement learning algorithm that attempts to approximate the Q function, which is a function that, given a state s and an action a, returns a real number that represents the return (or value) of state s when action a is taken from s. Q-learning was introduced in the PhD thesis "Learning from Delayed Rewards" (1989) by Watkins.

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2answers
107 views

When are Q values calculated in experience replay?

In experience replay, the update rule follows the loss: $$ L_i(\theta_i) = \mathbb{E}_{(s_t, a_t, r_t, s_{t+1}) \sim U(D)} \left[ \left(r_t + \gamma \max_{a_{t+1}} Q(s_{t+1}, a_{t+1}; \theta_i^-) - Q(...
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1answer
551 views

Why isn't my Q-Learning agent able to play tic-tac-toe?

I tried to build a Q-learning agent which you can play tic tac toe against after training. Unfortunately, the agent performs pretty poorly. He tries to win but does not try to make me 'not winning' ...
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0answers
53 views

How does one stack multiple observations in the input layer of a convolutional neural network?

The paper, Deep Recurrent Q-Learning for Partially Observable MDPs, talks about stacking multiple observations in the input of a convolutional neural network. How does this exactly work? Do the ...
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0answers
45 views

How to use Deep Q-Network with two-dimensional input? Hands-on Machine Learning 2

I'm studying with the book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow, and I'm trying to implement the Deep Q-Network example that can be found on Github but that the input ...
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1answer
84 views

Q-learning agent stuck at taking same actions

I have created my own RL environment where I have a 2-dimensional matrix as a state space, the rows represent the users that are asking for a service, and 3 columns representing 3 types of users; so ...
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0answers
25 views

Q-learning in Python (3D grid navigation): Values do not change between episodes

I am trying to implement a Q-learning algorithm in Python for a 3D gird world as part of an assignment, wherein the environment.py file defines the actions to be taken. So far I have tried several ...
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1answer
61 views

In the definition of the state-action value function, what is the random variable we take the expectation of?

I know that $$\mathbb{E}[g(X) \mid A] = \sum\limits_{x} g(x) p_{X \mid A}(x)$$ for any random variable $X$. Now, consider the following expression. $$\mathbb{E}_{\pi} \left[ \sum \limits_{k=0}^{\infty}...
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1answer
302 views

How to determine if Q-learning has converged in practice?

I am using Q-learning and SARSA to solve a problem. The agent learns to go from the start to the goal without falling in the holes. At each state, I can choose the action corresponding to the maximum ...
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1answer
176 views

Reinforcement learning simple problem: agent not learning, wrong action

I am pretty new to RL and I am trying to code a simple RL task with pytorch. The goal/task is the following: The initial state is $t_o$ and the agent takes an action $\Delta_t$: $t_o +\Delta_t = t_1$. ...
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1answer
110 views

Why is the reward function $\text{reward} = 1/{(\text{cost}+1)^2}$ better than $\text{reward} =1/(\text{cost}+1)$?

I have implemented a simple Q-learning algorithm to minimize a cost function by setting the reward to the inverse of the cost of the action taken by the agent. The algorithm converges nicely, but ...
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0answers
36 views

Pytorch and keras ddqn seem identical, only keras learns

I followed a tutorial for ddqn to beat pong, it beats it with a perfect score in keras, but trying to translate it to pytorch it doesn't learn at all. What am I missing? I pasted all the code for each ...
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1answer
64 views

Can we stop training as soon as epsilon is small?

I'm new to reinforcement learning. As it is common in RL, $\epsilon$-greedy search for the behavior/exploration is used. So, at the beginning of the training, $\epsilon$ is high, and therefore a lot ...
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1answer
164 views

Why do my rewards reduce after extensive training using D3QN?

I am running a drone simulator for collision avoidance using a slight variant of D3QN. The training is usually costly (runs for at least a week) and I have observed that reward function gradually ...
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1answer
211 views

Why can't we apply value iteration when we do not know the reward and transition functions, and how does Q-learning solve this issue?

I don't understand why we can't apply value iteration when don't know the reward and transition probabilities. In this lecture, the lecturer says it has to do with not being able to take max with ...
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2answers
137 views

How does one know that a problem is “model-free” in reinforcement learning?

Consider this slide from a Stanford lecture on reinforcement learning. It states that a model is the agent's representation of how the world changes in response to the agent's action. I've been ...
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0answers
48 views

Handling a Large Discrete Action Space in Deep Q Learning

I am attempting to solve a timetabling problem using deep Q learning. It could be thought of as a resource allocation problem to obtain some certificate of 'optimality'. However, how to define and ...
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1answer
103 views

Why is sampling non-uniformly from the replay memory an issue? (Prioritized experience replay)

I can't seem to understand why we need importance sampling in prioritized experience replay (PER). The authors of the paper write on page 5: The estimation of the expected value with stochastic ...
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0answers
24 views

Is it feasible to train a DQN with thousands of input ports?

I designed a DQN architecture for some problem. The problem has a parameter $m$ as the number of clients. In my situation, $m$ is large, $m\in\{100,200,\ldots,1000\}$. For this situation, the number ...
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1answer
53 views

What is the optimal exploration-exploitation trade-off in Q*bert?

I am training an RL agent with Deep Q-learning + Experience Replay on the Q*bert Atari environment. After 400,000 frames, my agent appears to have learned strategic information about the game, but ...
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1answer
194 views

Is there a logical method of deducing an optimal batch size when training a Deep Q-learning agent with experience replay?

I am training an RL agent using Deep-Q learning with experience replay. At each frame, I am currently sampling 32 random transitions from a queue which stores a maximum of 20000 and training as ...
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1answer
28 views

How is weighted average computed in Deep Q networks

I was going through the Sutton book and they said the update formula for Q learning comes from the weighted average of the returns I.e New estimate= old estimate +alpha*[returns- old estimate] So by ...
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1answer
1k views

What are the differences between Q-Learning and A*?

Q-learning seems to be related to A*. I am wondering if there are (and what are) the differences between them.
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2answers
306 views

What are some online courses for deep reinforcement learning?

What are some (good) online courses for deep reinforcement learning? I would like the course to be both programming and theoretical. I really liked David Silver's course, but the course dates from ...
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0answers
30 views

Customized food for persons based on their profile using Reinforcement learning

I am newbie to Reinforcement Learning, this is my idea - Agent(food provider) has to select a food based on the environment(based on the user profile). Here the reward will be given to the agent based ...
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1answer
71 views

When using experience replay in reinforcement learning, which state is used for training?

I'm slightly confused about the experience replay process. I understand why we use batch processing in reinforcement learning, and from my understanding, a batch of states is input into the neural ...
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0answers
63 views

Should I use the discounted average reward as objective in a finite-horizon problem?

I am new to reinforcement learning, but, for a finite horizon application problem, I am considering using the average reward instead of the sum of rewards as the objective. Specifically, there are a ...
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1answer
279 views

When do SARSA and Q-Learning converge to optimal Q values?

Here's another interesting multiple-choice question that puzzles me a bit. In tabular MDPs, if using a decision policy that visits all states an infinite number of times, and in each state, randomly ...
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0answers
50 views

How does DQN convergence work in reinforcement learning

In supervised learning we have an unbiased target value, but in reinforcement learning this isn’t the case The network predicts its own target value, now how exactly does it converge if the network ...
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0answers
32 views

How can deep Q-learning converge if the targets may not be correct?

In deep Q-learning, $Q(s, a)$ and $Q'(s, a)$ are predicted or estimated by the neural network itself. In supervised learning, the target value is a true unbiased value. However, this isn't the case in ...
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0answers
65 views

Strange behavior of Q-learning agent after being trained

I built a simple X*Y grid world environment to learn and then trained my agent over it. All worked fine and the agent learned as well. Let me give some detail about the environment. Environment: A ...
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1answer
81 views

How can I update my Q-table in Python?

I want to implement this function on a voice searching application: $$ Q(S, A) \leftarrow Q(S, A)+\alpha\left(R+\gamma Q\left(S^{\prime}, A^{\prime}\right)-Q(S, A)\right) $$ And also restricted to use ...
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1answer
79 views

How can I fetch ​exploration decay rate of an iterable Q-table in Python?

I have done creating the virtual environment, creating the Q-table, initializing the q-parameters, then I made a training module and stored it in a numpy array. ...
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0answers
70 views

Most of state-action pairs remain unvisited in the q-table

In building my first Q-learning algorithm for OpenAI gym's CartPole problem, many of my states remain unvisited. I believe it is the reason that my agent does not learn. Can I be told of the reasons I ...
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0answers
80 views

OpenAI gym's CartPole problem system does not learn

My OpenAI CartPole-v0 problem's implementation using basic Q-learning does not learn at all. I am a beginner and have implemented my first ever Q-learning from scratch after learning from tutorials. ...
4
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1answer
242 views

Why do DQNs tend to forget?

Why do DQNs tend to forget? Is it because when you feed highly correlated samples, your model (function approximation) doesn't give a general solution? For example: I use level 1 experiences, my ...
2
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1answer
72 views

Reinforcement learning with action consisting of two discrete values

I'm new to reinforcement learning. I have a problem where an action is composed of an order (rod with a required length) and an item from a warehouse (an existing rod with a certain length, which will ...
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0answers
165 views

Is the Bellman equation that uses sampling weighted by the Q values (instead of max) a contraction?

It is proved that the Bellman update is a contraction (1). Here is the Bellman update that is used for Q-Learning: $$Q_{t+1}(s, a) = Q_{t}(s, a) + \alpha*(r(s, a, s') + \gamma \max_{a^*} (Q_{t}(s', ...
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1answer
307 views

How can I change observation states' values in OpenAI gym's cartpole environment?

I am learning with the OpenAI gym's cart pole environment. I want to make the observation states discrete (with small stepsize) and for that purpose, I need to change two of the observations from [$ -\...
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3answers
467 views

Upper limit to the maximum cumulative reward in a deep reinforcement learning problem

Is there an upper limit to the maximum cumulative reward in a deep reinforcement learning problem? For example you want to train an DQN agent in an environment and you want to know what is the highest ...
2
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1answer
397 views

Why is the expected return in Reinforcement Learning (RL) computed as a sum of cumulative rewards?

Why is the expected return in Reinforcement Learning (RL) computed as a sum of cumulative rewards? Would it not make more sense to compute $\mathbb{E}(R \mid s, a)$ (the expected return for taking ...
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0answers
52 views

Prioritised Remembering in Experience Replay (Q-Learning)

I'm using Experience Replay based on the original Prioritized Experience Replay (PER) paper. In the paper authors show ~ an order of magnitude increase in data efficiency from prioritized sampling. ...
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1answer
141 views

What is the purpose of a Neural Network in Reinforcement Learning when we have a Q-learning update rule?

I'm confused as to the purpose of training a neural network (NN) for reinforcement learning (RL) tasks such as Gridworld. In RL tasks, namely q-learning, we have a q-learning update rule, which is ...
2
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1answer
46 views

What is convergence analysis, and why is it needed in reinforcement learning?

While reading a paper about Q-learning in network energy consumption, I came across the section on convergence analysis. Does anyone know what convergence analysis is, and why is convergence analysis ...
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1answer
699 views

Why do we need target network in deep Q learning? [duplicate]

I already know deep RL, but to learn it deeply I want to know why do we need 2 networks in deep RL. What does the target network do? I now there is huge mathematics into this, but I want to know deep ...
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0answers
43 views

How can I formulate a prediction problem (given labeled data) as an RL problem and solve it with Q-learning?

One of my friends sent me a problem he was working on lately, and I couldn't help but I wonder how could it be solved using Q-learning. The statement is as follows: Given the following datasets, the ...
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1answer
97 views

How does training for DQN work if messing up in the environment in costly?

Suppose that we want to train a car to drive in the real world and decide to use Reinforcement Learning (specifically, DQN) for that. I am a bit confused about how training generally works. Is it that ...
2
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1answer
56 views

How to predict Q-values based on stack of frames

I decided to train deep Q-learning agent based on getting raw pixels from environment.I have one particular problem:when I input stack of frames, suppose 4 consecutive frames, if action space is 6,...
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0answers
77 views

Tic-tac-toe: How would standard SARSA and Q-learning yield different results in the agent's behaviour?

I know this is deceptively simple. Tic tac toe is a well studied game for RL. Assume your agent is playing aggainst a strong opponent. I know you deal in after states. I know that in Q learning the ...
2
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1answer
85 views

q learning appears to converge but does not always win against random tic tac toe player

q learning is defined as: Here is my implementation of q learning of the tic tac toe problem: ...
2
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1answer
120 views

Implementing SARSA for a 2-stage Markov Decision Process

I am a bit confused as to how exactly I should be implementing SARSA (or Q-learning too) on what is a simple 2-stage Markov Decision Task. The structure of the task is as follows: Basically, there ...

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