Questions tagged [q-learning]
A model-free reinforcement learning technique.
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Deep Q-Learning: How are network parameters updated, and why consider episodes in the first place?
I'm trying to wrap my head around the implementation of deep $Q$-learning, and why we even consider episodes in the first place. The usual set-up is that we initialize some starting state $s_0$, then ...
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Dueling DQN with varying number of actions
I have an RL problem, where the number of actions depends on the state. Furthermore, each action-value computation requires action information in the form of a high-dimensional, continuous vector in ...
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find q-table for discrete action space
I am trying to use q-learning for a discrete observation space that is represented by:
buffer: list of 200 integer values in [0,10]
discard_counter: list of 200 integer values in [0, 4]
capacity: ...
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Problem of extreamly varied reward value in DDQN
I am trying to train my model by DDQN agent after creating a customized environment in gym. I am stating my hyper-parameters and other details here.
state shape = 5
action space = 0,1,2, ..., 100
...
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In Q-learning, why does Q index on both state and action?
In Q-learning, Q is an array of expected rewards for (state, action) combinations.
It seems to me the same result could be achieved while slightly simplifying the algorithm, if instead of associating ...
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In a finite horizon reinforcement learning problem, are the $Q$ and value functions dependent on time?
Typically the definition I see for the $Q$ and value functions is
$$
Q^\pi(s_t, a_t) = \mathbb{E}_\tau\left[\sum_{t'=t}^T\gamma^{t'-t}r(s_{t'}, a_{t'})\ |\ s_t, a_t\right] \\
V^\pi(s_t) = \mathbb{E}_\...
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Help with transitioning an existing DQN into a DRQN
Hi Data Science Stack Exchange community,
To preface this post, please let me know if I need to clarify any details to receive help and/or guidance. I am new to posting on Data Science Stack Exchange ...
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Bayesian state description in Reinforcement Learning
What's the best approach to feed a bayesian description of an observed state to a Reinforcement Learning agent?
Brief context: I have an agent situated in an environment, which it perceives through a ...
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Reinforcement Learning in a game against itself?
Let's we have a tictactoe design using RL against a random player. We can describe the system by enhancing and giving rewards to good actions. But what if the Rl model is played with itself? What ...
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Why DQN but no Deep Sarsa?
Why is DQN frequently used while there is hardly any occurrence of Deep Sarsa?
I found this paper https://arxiv.org/pdf/1702.03118.pdf using it, but nothing else which might be relevant.
I assume the ...
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How to Form the Training Examples for Deep Q Network in Reinforcement Learning?
Trying to pick up basics of reinforcement learning by self-study from some blogs and texts. Forgive me if the question is too basic and different bits that I understand are a bit messy, but even after ...
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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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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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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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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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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 ...
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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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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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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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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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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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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 ...
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"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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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?
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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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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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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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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 ...
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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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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 ...
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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 ...
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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 ...
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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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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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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 ...
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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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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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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 ...
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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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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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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:
...
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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 $...
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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 ...
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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 ...
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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 ...
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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 ...
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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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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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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 ...
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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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