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.

Filter by
Sorted by
Tagged with
1
vote
1answer
30 views

Can this be a possible deep q learning pseudocode?

I am not using replay here. Can this be a possible deep q learning pseudocode? ...
1
vote
0answers
13 views

Do RNN solves the need for LSTM and/or multiple states in Deep Q-Learning?

Introduction I am trying to setup a Deep Q-Learning agent. I have looked that the papers Playing Atari with Deep Reinforcement Learning as well as Deep Recurrent Q-Learning for Partially Observable ...
0
votes
1answer
21 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, ...
1
vote
0answers
11 views

Whats the correct loss function to use during deep Q-learning (discrete action space)

After playing around with normal Q-learning I have decided to switch to deep Q-learning and I have encountered this problem. As I understand, for a task with discrete action space, where there are 4 ...
2
votes
2answers
74 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 ...
2
votes
0answers
30 views

Is this a good approach to solving Atari's “Montezuma's Revenge”?

I'm new to Reinforcement Learning. For an internship, I am currently training Atari's "Montezuma's Revenge" using a double Deep Q-Network with Hindsight Experience Replay (HER). HER is supposed to ...
1
vote
1answer
29 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 ...
1
vote
1answer
24 views

How to use convolution neural network in Deep-Q?

I currently have a grid of pixels 20x20. Each pixel can be red green blue or black. So I have one hot-encoded the pixels giving a 20x20x4 array for each screen. For my Deep-Q Network, I have ...
2
votes
1answer
34 views

Intutitive explanation of why Experience Replay is used in a Deep Q Network?

I understand that Experience Replay is used for data efficiency reasons and to remove correlations in sequences of data. How exactly do these sequences of correlated data affect the performance of the ...
1
vote
0answers
50 views

How should I define the state space for this life science problem?

I would like to ask for a piece of advice with regard to Q-learning. I am studying RL and would like to do a basic project applied to life science and calculate the reward. I have been trying to get ...
2
votes
1answer
47 views

How is the expected value in the loss function of DQN approximated?

In Deep Q Learning the parametrized Q-functions $Q_i$ are optimised by performing gradient descent on the series of loss functions $L_i(\theta_i)= E_{(s,a)\sim p}[(y_i-Q(s,a;\theta_i))^2]$ , where ...
2
votes
1answer
32 views

Is there an advantage in decaying $\epsilon$ during Q-Learning?

If the agent is following an $\epsilon$-greedy policy derived from Q, is there any advantage to decaying $\epsilon$ even though $\epsilon$ decay is not required for convergence ?
0
votes
0answers
38 views

Understanding V- and Q-functions

Assume the existence of a Markov Decision Process consisting of: State space $S$ Action space $A$ Transition model $T: S \times A \times S \to [0,1]$ Reward function $R: S \times A \times S \to \...
2
votes
0answers
24 views

Evaluation a policy learned using Q - learning

I have been reading literature on reinforcement learning in healthcare. I am slightly confused between the policy evaluation for both SARSA and Q-learning. To my knowledge, I believe that SARSA is ...
2
votes
1answer
60 views

Taxi-v3 help. What is meant exactly by convergence of the algo, the highest reward and optimal action for every state?

I started learning about Q table from this blog post Introduction to reinforcement learning and OpenAI Gym, by Justin Francis, which has a line as below - After so many episodes, the algorithm will ...
1
vote
0answers
24 views

How to represent a state in a card game environment? (Wizard)

We are attempting to build an AI that manages to play the cardgame Wizard. So far er have a working network (based on the YOLO object-detection) that is abled to detect which cards are played. When ...
1
vote
1answer
29 views

Q-learning problem wrong policy

I am coding out a simple 4x4 grid game whereby the agent starts at a particular state and his aim is to reach the terminal state. The agent is supposed to avoid traps along the way and reach the end ...
2
votes
1answer
37 views

Do we need an explicit policy to sample $A'$ in order to compute the target in SARSA or Q-learning?

I would much appreciate if you could point me in the right direction regarding this question about targets for SARSA and Q-learning (notation: $S$ is the current state, $A$ is the current action, $R$ ...
1
vote
1answer
234 views

What is the difference between the epsilon greedy and softmax policies?

Could someone explain to me which is the key difference between the epsilon greedy policy and the softmax policy? In particular in the contest of SARSA and Q-Learning algorithms. I understood the main ...
1
vote
0answers
79 views

Expected SARSA, SARSA and Q-learning

I would much appreciate if you could point me in the right direction regarding this question about targets for approximate ...
3
votes
1answer
70 views

Why Monte Carlo epsilon-soft approach cannot compute $\max Q(s,a)$?

I am new to Reinforcement learning and am currently reading up on the estimation of Q $\pi(s, a)$ values using MC epsilon-soft approach and chanced upon this algorithm. The link to the algorithm is ...
4
votes
1answer
41 views

How can a DQN backpropagate its loss?

I'm currently trying to take the next step in deep learning. I managed so far to write my own basic feed-forward network in python without any frameworks (just numpy and pandas), so I think I ...
3
votes
1answer
54 views

Is the Q value updated at every episode?

I trying to understand the Bellman equation for updating the Q table values. The concept of initially updating the value is clear to me. What is unclear is the subsequent updates to the value. Is the ...
1
vote
0answers
16 views

N-tuple based tic tac toe diverges in temporal difference learning

I have n-tuple based tic tac toe. I already have perfect minimax player and perfectly trained table-based player. My n-tuple network consists of 8 different rows of 3 of the board as triplets having ...
2
votes
0answers
40 views

Q-learning: How to include a terminal state in updating rule?

I use Q-learning in order to determine the optimal path of an agent. I know in advance that my path is composed of exactly 3 states (so after 3 states I reach a terminal state). I would like to know ...
1
vote
0answers
45 views

Reinforcement learning for a 2D game involving two players

I'd like to create an AI for a 2D game involving two players fighting against each other. The map look something like this (The map is a NxN array somehow randomly generated): Basically the players ...
1
vote
1answer
31 views

Is the following the correct implementation of the Q learning algorithm for a neural network?

I just wanted to confirm that my understanding of Q learning was correct (with respect to a neural network). ...
1
vote
0answers
55 views

Why isn't this deep Q network agent for the snake game learning?

I wanted to combine this snake game with this DQN implementation I found in this article. First, I tried to change the NN's input layer to a 400 input. The game has a field of 20 times 20, so I ...
3
votes
1answer
89 views

Does using the softmax function in Q learning not defeat the purpose of Q learning?

It is my understanding that, in Q-learning, you are trying to mimic the optimal $Q$ function $Q*$, where $Q*$ is a measure of the predicted reward received from taking action $a$ at state $s$ so that ...
2
votes
0answers
28 views

How would one develop an action space for a game that is proprietary?

I'm currently trying to develop an RL that will teach itself to play the popular fighting game "Tekken 7". I initially had the idea of teaching it to play generally- against actual opponents with ...
3
votes
1answer
63 views

Can Google's patented ML algorithms be used commercially?

I just find that Google patents some of the widely used machine learning algorithms. For example: System and method for addressing overfitting in a neural network (Dropout?) Processing images using ...
1
vote
0answers
26 views

What is the complexity of policy gradient algorithms compared to discrete action space algorithms?

I am using a policy gradient algorithm (actor-critic) for wireless networks. The policy gradient-based algorithm helps because it considers continuous action space. But how much does a policy ...
2
votes
1answer
28 views

Why feed actions in later layer in Q network?

I read the DDPG paper, in which the authors state that the actions are fed only later to their Q network: Actions were not included until the 2nd hidden layer of Q. (Sec 7, Experiment Details) So ...
1
vote
0answers
37 views

Did I understand deep Q leaning right? (Implementation)

Gday guys, so I tried to implement my own enviroment and agent in order to fully understand DQNs. The enviroment is a dungeon with five states. actionspace = 2 statespace = 5 !!!Action a0 is ...
2
votes
1answer
117 views

Q Learning for FrozenLake environment not converging to V* values from Value Iteration

I am trying to learn tabular Q learning, value iteration using the classical algorithms (no neural networks) by using a table of states and actions. I was trying it out on FrozenLake environment in ...
0
votes
1answer
66 views

Model-based Reinforcement Learning algorithm for real-time robotics task

I'm quite a newbie when it comes to practically working with Deep Learning techniques, although I studied them quite a lot theoretically in the last months. However, now I'm facing my first practical ...
1
vote
0answers
14 views

TD losses are descreasing, but also rewards are decreasing, increasing sigma

I'm using Q-learning with some extensions such as noisy linear layers, n-steps and double DQN. The training, however, isn't that successful, my rewards are descreasing over time after a steep ...
2
votes
1answer
101 views

What happens to the optimal value function if the reward is multiplied by a constant?

What happens to the optimal action-value function, $q_*$ if the reward is multiplied by a constant $c$? Is the optimal action-value function also multiplied by such a constant?
1
vote
0answers
60 views

How is q-learning related to game trees?

At a first look, q-learning is a revolutionary strategy in realizing Artificial Intelligence. It has to do with finding a policy, a reward structure, neural networks for storing the q-table and a ...
6
votes
2answers
181 views

What does the symbol $\mathbb E$ mean in these equations?

I came across some papers that use $\mathbb E$ in equations, in particular, this paper: https://arxiv.org/pdf/1511.06581.pdf. Here is some equations from the paper that uses it: $Q^\pi \left(s,a \...
5
votes
1answer
57 views

Can exogenous variables be state features in reinforcement learning?

I have a question about state representation of Q-learning or DQN algorithm. I'm still a beginner of RL, so I'm not sure that is it suitable to take exogenous variables as state features. For example,...
2
votes
3answers
123 views

Reinforcement learning: How to deal with illegal actions?

I'm a beginner of RL and currently trying to make DQN agent that can act optimally in a simple situation. In the situation agent should decide at what rate to charge or discharge the electrical ...
4
votes
1answer
102 views

Is tabular Q-learning considered interpretable?

I am working on a research project in a domain where other related works have always resorted to deep Q-learning. The motivation of my research stems from the fact that the domain has an inherent ...
1
vote
1answer
80 views

Deep Q Learning for Simple Game Not Effective

This is a follow-up question about one I asked earlier. The first question is here. Basically, I have a game where a paddle moves left and right to catch as much "food" as possible. Some food is good (...
1
vote
1answer
89 views

Deep Q Learning Algorithm for Simple Python Game makes player stuck

I made a simple Python game. A screenshot is below: Basically, a paddle moves left and right catching particles. Some make you lose points while others make you gains points. This is my first Deep Q ...
1
vote
0answers
40 views

Unique game problem (ML, DP, PP etc)

Looking for a solution to my below game problem. I believe it to require some sort of reinforcement learning, dynamic programming, or probabilistic programming solution, but am unsure... This is my ...
2
votes
1answer
36 views

Will the target network, which is less trained than the normal network, output inferior estimates?

I'm having some trouble understanding some parts of the usage of target networks. I get that having the same network predict the state/action/advantage values for both the current networks can lead ...
3
votes
0answers
83 views

How can I use Q-learning for inventory decision making?

I am trying to model operational decisions in inventory control. The control policy is base stock with a fixed stock level of $S$. That is replenishment orders are placed for every demand arrival to ...
0
votes
1answer
54 views

Probabilistic action selection in pursuit algorithm

In the Pursuit algorithm (to balance exploration and exploitation), the greedy action has a probability say $p_1$ (updated every episode) of being selected, while the rest have a probability $p_2$ (...
3
votes
1answer
91 views

Is it possible to have a dynamic $Q$-function?

I am trying to use Q-learning for energy optimization. I only wish to have states that will be visited by the learning agent, and, for each state, I have a function that generates possible actions, so ...