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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25 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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26 views

Pytorch Deep q network not learning and step not stepping towards target

I am trying to create a simple deep q network for rl with conv2d layers. I can’t figure out what I am doing wrong, and the only thing I can see that doesn’t seem right is when I get the model ...
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1answer
93 views

Why Q2 is a more or less independant estimate in Twin Delayed DDPG (TD3)?

Twin Delayed Deep Deterministic (TD3) policy gradient is inspired by both double Q-learning and double DQN. In double Q-learning, I understand that Q1 and Q2 are independent because they are trained ...
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1answer
68 views

How to compute the target for double Q-learning update step?

I've already read the original paper about double DQN but I do not find a clear and practical explanation of how the target $y$ is computed, so here's how I interpreted the method (let's say I have 3 ...
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1answer
1k views

How should I model all available actions of a chess game in deep Q-learning?

I just read about deep Q-learning, which is using a neural network for the value function instead of a table. I saw the example here: https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html and ...
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27 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
66 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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20 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
39 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
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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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2answers
52 views

How to apply Q-learning when rewards is only available at the last state?

I have a scheduling problem in which there are $n$ slots and $m$ clients. I am trying to solve the problem using Q-learning so I have made the following state-action model. A state $s_t$ is given by ...
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36 views

Non-Neural Network algorithms for large state space in zero sum games

I was reading online that Tic Tac Toe has a state space of 3^9 = 19,683. From my basic understanding, this sounds too large to use with Q Learning, as the Q table would be huge? If that is the case, ...
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1answer
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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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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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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
47 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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54 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
54 views

How does the optimization process in hindsight experience replay exactly work?

I was reading the following research paper Hindsight Experience Replay. This is the paper that introduces a concept called Hindsight Experience Replay (HER), which basically attempts to alleviate the ...
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1answer
81 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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33 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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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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1answer
90 views

What exactly is the advantage of DDQN over DQN

I started looking into DDQN and apparently the difference is we use our Online network for action selection, And we use our target network for outputting the Q values, I don’t quite get how this is ...
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61 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
80 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
73 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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62 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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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. ...
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1answer
174 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 ...
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1answer
68 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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139 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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Predict probability of user making a conversion

My dear friends, In the past couple of years I read a lot about AI with JS and some libraries like TensorFlow. I have great interest in the subject but never used it on a serious project. However, ...
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1answer
51 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
256 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 ...
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1answer
64 views

How can I model this problem of delivering assets by choosing a route with reinforcement learning?

I would like to build a model based on reinforcement learning (RL) for the following scenario Recommend the best route (of cities listed for a given country) that satisfies the required criteria (...
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1answer
94 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
49 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
225 views

Deep Reinforcement Learning: Rewards suddenly dip down

I am working on a deep reinforcement learning problem. The policy network has the same architecture as the one Deepmind published in 'Playing Atari with Deep Reinforcement Learning'. I am also using ...
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1answer
67 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 ...
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1answer
40 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
56 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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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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3answers
1k views

Why doesn't Q-learning converge when using function approximation?

The tabular Q-learning algorithm is guaranteed to find the optimal $Q$ function, $Q^*$, provided the following conditions (the Robbins-Monro conditions) regarding the learning rate are satisfied $\...
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1answer
91 views

How to implement RAM versions of Atari games

I have coded the breakout RAM version, but, unfortunately, its highest reward was 5. I trained it for about 2 hours and never reached a higher score. The code is huge, so I can't paste here, but, in ...
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2answers
115 views

What is the difference between return and expected return?

At a time step $t$, for a state $S_{t}$, the return is defined as the discounted cumulative reward from that time step $t$. If an agent is following a policy (which in itself is a probability ...
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1answer
46 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 ...
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1answer
44 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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24 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 ...
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1answer
72 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: ...
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33 views

Why Q-Learning and SARSA have terrible performance?

I am trying to solve a MDP problem with almost 50 states and 60 actions with Q-Learning or SARSA. However, the performance of both algorithms is terrible and cannot find the optimal policy found by ...

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