Questions tagged [q-learning]
A model-free reinforcement learning technique.
128
questions
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6 views
Q-learning episode and relation to convergence in MY scenario?
I used Q-learning for routing. I have used the Bellman equation. I have certain other technical aspects in the code that add some novelty. But I have mixed doubts regarding episode and corresponding ...
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0answers
14 views
How to create transition probability (state) for q-learning algorithm designed to control traffic light system using python?
I am trying to create a q learning algorithm to control traffic light systems.
I am representing the state with a matrix :
...
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1answer
42 views
Deep Q-learning
I am working on the DDQN algorithm which is given in the following paper.
I am facing a problem with the Q value.
The author calculate Q value by this
Q(s, a; θ , α, β) = V(s; θ , β) + A(s, a; θ , α).
...
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0answers
37 views
Q value is estimated under state V value and action A value for DDQN
How Q value is estimated under state V value and action A value.
Given the below DDQN algorithm, the deep network is divided into two parts on the end layer, including state value function V(s) which ...
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1answer
37 views
Understanding DQN Algorithm
Im studying the deep q learning algorithm. You can see it in the picture here:
DQN
I have a few questions about the deep q learning algorithm.
What do they mean with row 14: ...
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31 views
Am I using this neural network in a wrong way?
I'm trying to solve a RL problem; the Contextual Bandit problem using Deep Q Learning. My data is all simulated. I have this environment:
...
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0answers
22 views
How to construct Q-table for complex, large and dynamic spaces in python?
I am trying to construct a Q-table.
I have state space and action space. State space consists of large number of complex and dynamic number of elements, but discrete.
Theoretically, I understood ...
2
votes
1answer
57 views
Efficient way to tackle card games with many q-table states?
I'm currently in the process of developing an AI for a popular card game here in Germany (called "Schafkopf"). Obviously, one could try to find a perfect strategy with the help of some game ...
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20 views
snake reinfocement learning does not learn
I’m new to reinf learning, and I’m trying this code, but the snake always goes right without learning, I can’t find the bug, could you help me?
THE SNAKE CLASS
...
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0answers
16 views
Random exploration of an arbitrary agent (or dumb agent) in Deep RL
I'm trying to implement an agent in RL and I was wondering if it's pertinent to take random actions in DeepRL. I see that in traditional Q-learning that sometimes we take random actions to encourage ...
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0answers
29 views
Deep-DQN implementation that works with (state,action) pairs
I'm moderately new to RL(Reinforcement learning) and trying to solve a problem by using a deep Q learning agent (trying a bunch of algorithms) and I don't want to implement my own agent (I would ...
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0answers
12 views
In reinforcement learning, continue update weights after training?
Assuming an rl agent that has to be trained, and then you can mainain same weights over episodes: in a single episode, you have to firtly backup weights, udpate continuosly weights in the episodes, ...
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1answer
50 views
In DQN, why not use target network to predict current state Q values?
In DQN, why not use target network to predict current state Q values, and not only next state q values?
In doing a basic dq learning algorithm with nn from scratch, with replay memory, and minibatch ...
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2answers
41 views
Zero Padding in Convolutional neural network
We use Convolutional neural network because it by design learns features that generalize over spatial location , so when using conv operation it reduce image size and that what we hope to have so we ...
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0answers
16 views
Convolutional neural network with Deep Q learning in games
What does
"the average magnitude of maximal action value output by the network"
tell us? I mean if we plot this graph, is it good to start as low value and then increase until it goes in a ...
3
votes
1answer
105 views
Exploration in Q learning: Epsilon greedy vs Exploration function
I am trying to understand how to make sure that our agent explores the state space enough before exploiting what it knows. I am aware that we use epsilon-greedy approach with a decaying epsilon to ...
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0answers
231 views
Reinforcement Learning in continuing tasks (non-episodic)
I'm looking for an algorithm for Deep Reinforcement Learning in non-episodic or continuing tasks. To be explicit, I'm looking for an algorithm that allows the agent to learn online without separate ...
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0answers
49 views
DQN CartPole-v1 neural network doesn't optimize
I'm doing my first dnq algorithm, I'm trying to build a dnq agent, and neural network from scratch, but it seems that neural network doesn't optimize, I did 2 hidden layers, with ReLU, and the output ...
2
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0answers
27 views
"Learning" algorithm to use when future depends on past events (MDP property not met)
There are around 5 different retirement plans available in my country. People can pick from them freely. I would like to create a solution that would try to predict the best plan(s) given a particular ...
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0answers
19 views
How to use binary numbers and plain floats in the same neural network input for reinforcement learning?
I'm modeling a system whose configuration can be represented by a binary array ([1,0,0,1] or [0,1,0,0], for example), and the agent can move on a 2D space (thus having 3 DoFs), and the action the ...
1
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1answer
36 views
On what principle did Google's DeepMind learn to walk?
I just saw this video on Youtube.
On what principle did Google's DeepMind learn to walk?
Was it Q-Learning or a Genetic Algorithm or Policy Gradient?
2
votes
1answer
49 views
Dimensionality of the target for DQN agent training
From what I understand, a DQN agent has as many outputs as there are actions (for each state). If we consider a scalar state with 4 actions, that would mean that the DQN would have a 4 dimensional ...
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25 views
What are the differences between DQN DDQN and Dueling Q network?
What are the differences between DQN DDQN and Dueling Q network?
And how they expressed in the agent's behaviour? (For example in an Open AI Gym environment)
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0answers
75 views
Deep Q-learning in non-episodic tasks
I want to use Deep Q-learning (specifically DDQL by Hasselt et al. 2015, but it is the same principle) in a non-episodic task (continuing). I know that it is possible to use Q-learning in continuing ...
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0answers
75 views
DQN DDQN and 3DQN differences?
I'm doing a course on reinforcement learning, and one of our tasks is to implement an agent on the Lunar lander continuous V2 environment from openAI gym.
In order to solve the continuous problem, I ...
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0answers
10 views
Implementing DQN - Pylessons.com article - Queries [closed]
I have followed the below link to implement DQN Algorithm
https://pylessons.com/CartPole-reinforcement-learning/
Can someone explain me:
Why do we need to have else condition in act function during ...
2
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1answer
125 views
Does convergence equal learning in Deep Q-learning?
In my current research project I'm using the Deep Q-learning algorithm. The setup is as follows: I'm training the model (using Deep Q-learning) on a static dataset made up of experiences extracted ...
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17 views
How can my loss be stable while the gradient keeps growing?
I have been working on an Offline/Batch Reinforcement Learning problem where I am using a BCQ-DDQN model as a Q-table. The model input is a state of 8 dimensions, and the output is a vector of Q-...
0
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1answer
43 views
How to report results of RL with high variance?
I run Q-learning and SARSA algortihms on the same problem but the results fluctuate heavily and when I draw them, there is no smooth graph. How should I repost the results? I run algorithms for 500 ...
2
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0answers
82 views
Offline/Batch Reinforcement Learning: Doubly Robust Off-policy Estimator takes huge values
Context:
My team and I are working on a RL problem for a specific application. We have data collected from user interactions (states, actions, etc.).
It is too costly for us to emulate agents. We ...
1
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1answer
116 views
Definition of the Q* function in reinforcement learning
I'm making my way through Sutton's Introduction to Reinforcement Learning. He gives the definition of the $q_*$ function as follows
$$
q_*(a) = \mathbf{E}[R_t | A_t = a]
$$
where $A_t$ is the action ...
2
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0answers
24 views
Reference implementation of q-learning in Python
I'm a machine learning newbie, trying to learn Q-learning. I read a few texts and I get the general gist, but what I'd really love to see is a simple example of a Q-learning algorithm in Python that I ...
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1answer
60 views
Different Initial Q-Values in Q-Learning
When working with Q-Learning, what is the difference between having a Q_0(a) with all values zero, random or optimistic?
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1answer
66 views
When should I use normal Q learning over a DQN?
From this article here, it says that using a tabular Q function is less scalable than a deep Q network. I assume that this means that the Q table approach works for some environments, but once they ...
0
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1answer
103 views
Deep Q-learning, how to set q-value of non-selected actions?
I am learning Deep Q-learning by applying it to a real world problem. I have been through some tutorials and papers available online but I counldn't figure out the solution for the following problem ...
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0answers
15 views
Wich activation function for DQL
After many research, I still can't find a neat answer about this question:
When I found the loss of my state-action pair. I'm only backpropagating that loss true the network and setting all other ...
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1answer
166 views
Is this a valid stability concern/improvement for DQN/DDQN reinforcement training?
As you all know, DQN or DDQN are known for "unstable training".
Let's use the well known "CartPole". The agent has to balance the stick and gets a reward of +1 per frame. You can reach the 195 ...
0
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1answer
192 views
How to save and load a Q-Learning Agent
I know this may sound nooby, but how do I save a Deep Q-Learning agent's progress? I mean when I close at i.e. episode 500 when my agent is trained and I restart (in my case a pygame) my agent is ...
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0answers
102 views
Reward(t) vs. Reward(t+1) ? Reinforcement Learning, Q-learning
In "Reinforcement Learning, An Introduction; Richard S. Sutton and Andrew G. Barto" is written on page 70:
3.1 The Agent–Environment Interface
At each time step t, the agent receives some ...
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0answers
97 views
Q-learning, state transition, immediate rewards (trading logic)
I've been thinking about how to correctly calculate rewards for several weeks now.
Here is a grid example:
...
2
votes
1answer
37 views
Markov Decision Process representation
I'm attempting to model a simple process using a Markov Decision Process.
Let $A$ be a set of $3$ actions : $ A \in \{b,s\}$.
$T(s,a,s')$ represents the probability of if in state $s$ , take action $...
1
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0answers
26 views
help understanding deep Q learning algorithm from deep mind paper
I'm studying this paper https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf, and I have a question about proposed algorithm(it appears on page 7 of the paper):
The problem I see is ...
1
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1answer
44 views
How is the target_f updated in the Keras solution to the Deep Q-learning Cartpole/Gym algorithm?
There's a popular solution to the CartPole game using Keras and Deep Q-Learning:
https://keon.github.io/deep-q-learning/
But there's a line of code that's confusing, this same question has been asked ...
2
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2answers
132 views
Deep Q Network gives same Q values and doesn't improve
I'm trying to build a deep Q network to play snake. I've run into an issue where the agent doesn't learn and its performance at the end of the training cycle is to repeatedly kill itself. After a bit ...
2
votes
1answer
428 views
Would Deep Q Learning work for a finite horizon problem?
I want to apply Deep Q Learning to a problem, which has a clear finite horizon definition, like:
$$V(s) = \mathbb{E}[r_1 + r_2]$$
Since the horizon is finite, I do not use reward discounting. My ...
2
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0answers
53 views
Incentivizing curiosity in a sparse reward environment
I'm quite new to reinforcement learning, but have been exploring different kinds of architectures (DQN, dueling DQN, actor critic, etc.) and evaluating their ability to solve certain problems. The ...
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0answers
23 views
Deep Q Learning - training slows down significantly
I'm trying to build a deep Q network to play snake. I designed the game so that the window is 600 by 600 and the snake's head moves 30 pixels each tick. I implemented the DQN algorithm with memory ...
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0answers
276 views
Reducing the training time of an RL agent
I am trying to develop an rl agent using DQN algorithm.During training, the agent interacts with environment which is a simulated one.Each episode takes around 10 mins to run. This way if want my ...
1
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1answer
123 views
How to formulate reward of an rl agent with two objectives
I have started learning reinforcement learning and trying to apply it for my use case. I am developing an rl agent which can maintain temperature at a particular value, and minimize the energy ...
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0answers
46 views
Keras high loss and high accuracy in gk bot with reinforcement learning?
I'm making goal-keeper bot in haxball game. It worked well when i trained less but i worked worse when i trained more.
Last reinforcement state: 5160 episode - 4171281 steps - 0.05 epsilon:
Last fit ...
