Newest 'q-learning' Questions

1

vote

1answer

34 views

Q-learning when minimising a total cost instead of maximising a total reward

I have a decision problem where the results are measured as a cost that I want to minimise. It seems like a good fit to Q-learning, but I am not sure how to adjust it to deal with a cost instead of a ...

asked Jul 30 at 12:14

EArwa

154 bronze badges

0

votes

1answer

32 views

Deep Q-Learning for physical quantity: q-values distribution not as expected

Setting I am trying to learn a specific physical quantity (radiance) inside a 3D scene with Deep Q-Learning. Just to give a quick overview, my agent shoots rays inside the scene: the reward is the ...

deep-learning reinforcement-learning q-learning dqn

asked Jul 29 at 9:24

maurocomi

547 bronze badges

0

votes

0answers

10 views

What's the relationship amoung Temporal Difference and Policy Gradient, Deep Q learning?

I've gone through some comparisons between MC and TD to estimate $V_{\pi_\theta}(s)$. However, it seems to be not only a method to estimate V, but also a mechanism that can improve the update ...

machine-learning reinforcement-learning q-learning policy-gradients

asked Jul 22 at 10:46

KningTG

1

0

votes

1answer

11 views

Why can't Policy Gradient Algorithm be seen as an Actor-Critic Method?

During the equation deducing in policy gradient algorithm(e.g., REINFORCE), we are actually using an expectancy of total reward, which we try to maximize. $$\overline{R_\theta}=E_{\tau\sim\pi_\theta}[...

machine-learning reinforcement-learning q-learning policy-gradients actor-critic

asked Jul 22 at 9:54

KningTG

1

vote

1answer

28 views

DQN - target values vs action values?

I'm trying to understand the difference between target-values and action-values in Deep Q Networks. From what I understand, action-value tries to approximate the reward of a given action (at some ...

deep-learning reinforcement-learning q-learning

asked Jul 21 at 17:42

Alter

1134 bronze badges

3

votes

1answer

118 views

Why could my DDQN get significantly worse after beating the game repeatedly?

I've been trying to train a DDQN to play OpenAI Gym's CartPole-v1, but found that although it starts off well and starts getting full score (500) repeatedly (at around 600 episodes in the pic below), ...

python deep-learning reinforcement-learning q-learning openai-gym

asked Jul 20 at 8:06

Danny Tuppeny

1185 bronze badges

1

vote

1answer

92 views

Q table creation and update for dynamic action space

I am trying to implement a Q-learning algorithm for energy optimization. It is a finite MDP with states represented as 6 dimensional vectors of integers. The number of discrete values in each index of ...

q-learning

asked Jul 16 at 14:41

EArwa

154 bronze badges

1

vote

1answer

25 views

Deep q learning looping states

I am a little bit confused about deep q learning. As you know, in deep q learning, we update state action values according to the cumulative reward of specific action state pairs. However, in some ...

q-learning

asked Jul 3 at 17:52

uğur yıldırım

183 bronze badges

1

vote

1answer

46 views

Alternative approach for Q-Learning

I have a question related to an alterative Q-Learning approach. I'd like to know if this already exists and I am not aware of it, or it doesn't exist because there are theoretical problems behind it. ...

machine-learning reinforcement-learning q-learning

asked Jun 22 at 8:23

maurocomi

547 bronze badges

1

vote

1answer

74 views

Difference between Dueling DQN and Double DQN?

I have read some articles, but still can not figure out the difference between the Dueling DQN and Double DQN? What exactly is the difference between them? Also, Does Dueling DQN need to be built on ...

reinforcement-learning q-learning dqn deep-network

asked May 31 at 17:46

Edamame

6252 gold badges9 silver badges17 bronze badges

0

votes

0answers

5 views

Deep Q Learning state dimensionality

How important is the dimensionality of each state for Deep Q Learning? I have a set of 15 unique playing cards from a deck of 52 playing cards. A given state is represented by the respective card ...

deep-learning dimensionality-reduction q-learning

asked May 16 at 6:58

Harry Stuart

1063 bronze badges

2

votes

0answers

35 views

Reinforcement Learning using PPO2 in openai gym retro, mario not learning the clear the easy episode

I am training mario game in retro using ppo2 baselines for some time. I have tried level3 and level1 too. But even after full training when I play using saved checkpoints, the mario is not able to ...

reinforcement-learning q-learning openai-gym

asked May 11 at 10:46

Sandeep B

14610 bronze badges

0

votes

0answers

14 views

Reinforcement Learning - Q Learning - Number of Steps to Decrease?

I have an implementation of the Q-Learning algorithm intended to solve the racetrack problem. I have noticed that the initial amount of steps needed to solve the problem is somewhere between 3000-...

machine-learning reinforcement-learning algorithms q-learning

asked May 9 at 1:18

Jerry M.

1084 bronze badges

1

vote

0answers

39 views

Intuition behind the loss function in Deep Q learning?

I'm currently following a tutorial but I got stuck at the deep Q learning model. According to my understanding of neural networks they predict an approximate function for the inputs given with the ...

deep-learning reinforcement-learning loss-function q-learning

asked May 5 at 12:34

anjani

62 bronze badges

2

votes

1answer

22 views

If the set of all possible states changes each time, how can Q-learning “learn” anything?

I found this resource that explains q-learning with a very simple example. Make it a 2D problem, a rectangle instead of a line, and it's still simple. The only difference is that now there are 2 more ...

reinforcement-learning q-learning

asked May 4 at 21:40

Pigna

1333 bronze badges

2

votes

1answer

25 views

Representing state in Q-Learning

I have a fairly simple game in which I wish to use Q-learning to train an agent, but I have some questions regarding state representation. I'm new to RL so bare with me: If you have a game where you ...

reinforcement-learning beginner q-learning

asked May 4 at 9:09

Ross Kohler

111 bronze badge

0

votes

0answers

40 views

Why is the reward fluctuating for Double Q-Learning?

I am trying to implement Double Q-Learning using neural networks from the Keras library. When I first tried Simple DQN, the graph of the reward was fluctuating a lot so, I implemented a Double DQN. ...

deep-learning reinforcement-learning q-learning

asked Apr 20 at 8:19

Gauranga Das

12 bronze badges

0

votes

1answer

115 views

Q-Learning experience replay: how to feed the neural network?

I'm trying to replicate the DQN Atari experiment. Actually my DQN isn't performing well; checking another one's codes, I saw something about experience replay which I don't understand. First, when you ...

python reinforcement-learning q-learning dqn keras-rl

asked Apr 11 at 14:13

Joaquin

12 bronze badges

0

votes

0answers

18 views

Different algorithms categorized in reinforcement learning

(Originally asked at cross validated forum: https://stats.stackexchange.com/questions/401615/different-algorithms-categorized-in-reinforcement-learning) For some time I am going through reinforcement ...

reinforcement-learning q-learning

asked Apr 8 at 20:28

Sandeep B

14610 bronze badges

2

votes

1answer

187 views

DQN fails to find optimal policy

Based on DeepMind publication, I've recreated the environment and I am trying to make the DQN find and converge to an optimal policy. The task of an agent is to learn how to sustainably collect apples ...

reinforcement-learning q-learning dqn convergence deepmind

asked Apr 1 at 1:23

macwiatrak

113 bronze badges

0

votes

0answers

14 views

Is reward accumulated during a play iteration when performing SARSA?

I've been having issue with getting my DQN to converge to a good solution for snake. Regardless of the different types of reward functions I've tried, it seems that the snake is indefinitely going ...

machine-learning deep-learning reinforcement-learning q-learning dqn

asked Mar 29 at 1:44

DevarakondaV

164 bronze badges

0

votes

1answer

37 views

Openai Spaces for a modified environment

I have a 2-dimensional array of normalized data. I am using space = np.array([0,1,...366],[0,0.000001,.....1]) I need to fit this as an observation space in ...

python deep-learning reinforcement-learning q-learning openai-gym

asked Mar 17 at 16:29

Karthik Rajkumar

306 bronze badges

0

votes

0answers

6 views

Q learning transition matrix trouble

First, apologies if this isn't the right forum for my question -- let me know if there's a better place. I'm trying to implement Q learning on a two-dimensional grid world, where the agent has four ...

machine-learning q-learning

asked Jan 30 at 1:14

Elizabeth Han

1012 bronze badges

0

votes

0answers

23 views

Q-Learning where some actions are more difficult to predict than others

I'm trying to train a deep Q network to optimize play in a game. For simplicity, let's say my game only has two actions, A and B. The reward distribution for A is somewhat uniform in the range -1 to ...

machine-learning deep-learning reinforcement-learning q-learning

asked Jan 29 at 20:25

ahbutfore

111 bronze badge

0

votes

1answer

43 views

Why is “next state” kept in RL experience replay?

Following this explanation on what is experience replay (and others), I noticed an experience element is defined as $e_t = (s_t,a_t,r_t,s_{t+1})$ My question is, why do we need the ...

machine-learning reinforcement-learning q-learning policy-gradients

asked Jan 23 at 18:03

Gulzar

1137 bronze badges

0

votes

0answers

10 views

Is every value-iteration off-policy DP is a Q-learning?

Is it true to say that every value-iteration off-policy DP is a Q-learning technique. or there are many more specific definition for it?

q-learning

asked Jan 22 at 19:29

user10296606

3751 silver badge13 bronze badges

1

vote

0answers

24 views

What is the meaning of the Variant Q-learning and To what INPUT and OUTPUT refer? in Abstract of DeepMind DQN paper 2013

-INPUT and OUTPUT OF ATARI DQN: In the abstract paragraph of the DQN work by DeepMind https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf it has written: " We present the first deep learning model to ...

deep-learning reinforcement-learning q-learning dqn deep-network

asked Jan 22 at 10:36

user10296606

3751 silver badge13 bronze badges

2

votes

1answer

349 views

What is the difference between dynamic programming and Q-learning?

What is the difference between the DP-based algorithm and Q-learning?

reinforcement-learning q-learning dynamic-programming

asked Jan 21 at 5:52

user10296606

3751 silver badge13 bronze badges

0

votes

1answer

53 views

How to represent an image as state in a Q-table

I'm trying to do Q-learning with the Atari games using the gym python's package. I want to use the image as the state of my algorithm, but I came up with a doubt: ...

reinforcement-learning q-learning openai-gym

asked Dec 31 '18 at 18:22

Neoares

1185 bronze badges

0

votes

1answer

37 views

How does Q-Learning deal with mixed strategies?

I'm trying to understand how Q-learning deals with games where the optimal policy is a mixed strategy. The Bellman equation says that you should choose $max_a(Q(s,a))$ but this implies a single unique ...

machine-learning reinforcement-learning q-learning

asked Dec 20 '18 at 17:48

Thomas Johnson

3251 gold badge2 silver badges10 bronze badges

1

vote

1answer

64 views

Why not use max(returns) instead of average(returns) in off-policy Monte Carlo control?

As I understand it, in reinforcement learning, off-policy Monte Carlo control is when the state-action value function $Q(s,a)$ is estimated as a weighted average of the observed returns. However, in ...

reinforcement-learning q-learning monte-carlo

asked Dec 20 '18 at 10:52

Meldgaard

62 bronze badges

1

vote

1answer

175 views

Reinforcement learning: negative reward (punish) illegal actions?

If you train an agent using reinforcement learning (with Q-function in this case), should you give a negative reward (punish) if the agent proposes illegal actions for the presented state? I guess ...

machine-learning reinforcement-learning q-learning

asked Dec 2 '18 at 10:04

BigBadMe

3252 silver badges13 bronze badges

1

vote

1answer

772 views

IndexError: index 804 is out of bounds for axis 0 with size 800

i installed a self driving car project from superdatascience site , when i open the map using terminal after a while the map window close up or it closes directly after i maximize the map window and ...

reinforcement-learning python q-learning

asked Nov 5 '18 at 15:05

R.SH

1

vote

1answer

46 views

Will reinforcement learning work if states wont get repeated again?

I am working on a information retrieval model where the user enters a query and the model has to retrieve 3 most relevant FAQ pairs.I am collecting implicit feedback in terms of page clicks etc.What I ...

machine-learning deep-learning reinforcement-learning q-learning dqn

asked Oct 24 '18 at 11:57

Charan Reddy

1061 bronze badge

1

vote

0answers

81 views

Dueling DQN - Calculation of Q-value

I'm trying to implement a Double Dueling DQN on LunarLander and I'm facing an issue as my model is not learning so I'm trying to debug the graph and this leads me to a question regarding the ...

reinforcement-learning q-learning

asked Oct 19 '18 at 15:53

Nicolas M.

1065 bronze badges

1

vote

1answer

65 views

What's going wrong with my Tic Tac Toe Q-Learning Alghoritm?

my Q-learning alghoritm currently choices the "sub optimal" option and not the best one. ...

python q-learning

asked Oct 12 '18 at 20:11

CuriousPanda

63 bronze badges

1

vote

1answer

156 views

What is the immediate reward in value iteration?

Suppose you're given an MDP where rewards are attributed for reaching a state, independently of the action. Then when doing value iteration: $$ V_{i+1} = \max_a \sum_{s'} P_a(s,s') (R_a(s,s') + \...

reinforcement-learning q-learning

asked Oct 8 '18 at 14:38

Dominus

306 bronze badges

5

votes

1answer

2k views

Reinforcement learning: decreasing loss without increasing reward

I'm trying to solve OpenAI Gym's LunarLander-v2. I'm using the Deep Q-Learning algorithm. I have tried various hyperparameters, but I can't get a good score. Generally the loss decreases over many ...

reinforcement-learning q-learning

asked Sep 4 '18 at 12:06

Atuos

1625 bronze badges

1

vote

1answer

2k views

RL Advantage function why A = Q-V instead of A=V-Q?

In RL Course by David Silver - Lecture 7: Policy Gradient Methods, David explains what an Advantage function is, and how it's the difference between Q(s,a) and the V(s) Preliminary, from this post: ...

reinforcement-learning q-learning variance

asked Sep 1 '18 at 3:08

Kari

8445 silver badges26 bronze badges

2

votes

1answer

51 views

How we can have RF-QLearning or SVR-QLearning (Combine these algorithm with a Q-Learning )

How we can have RF-QLearning or SVR-QLearning (Combine these algorithm with a Q-Learning )? I want to replace the DNN section of Qlearning with a RF or SVR but the problem is that there is no clear ...

random-forest reinforcement-learning q-learning dqn svr

asked Aug 31 '18 at 18:01

user10296606

3751 silver badge13 bronze badges

1

vote

0answers

143 views

Deep Q-Learning with large number of actions

I'm using DQN with large number of actions in [0, 10000, step = 1000]. This means I have an action space of size 11 (including 0 and 10000). Action space is still discrete. My problem is that, instead ...

machine-learning deep-learning reinforcement-learning q-learning dqn

asked Aug 16 '18 at 6:04

Krishna Nevase

256 bronze badges

5

votes

1answer

97 views

Representing similar states in reinforcement learning?

Let's say I'd like to design a Q learning algorithm that learns to play poker. The number of different possible States is very large, but a lot are very similar: for example, if the initial state ...

reinforcement-learning q-learning

asked Aug 5 '18 at 21:01

shakedzy

3943 silver badges20 bronze badges

2

votes

1answer

87 views

Calculate Q parameter for Deep Q-Learning applied to videogames

I am working on Deep Q-learning applied to Snake, and I am confused on the methodology. Based on the DeepMind paper on the topic and other sources, the Q-value with the Bellman equation needs to be ...

machine-learning deep-learning reinforcement-learning q-learning

asked Jul 22 '18 at 13:46

maurocomi

547 bronze badges

1

vote

1answer

69 views

Tflearn “nan” weight matrices

I wanted to build a DQN. So I followed this code and watched some videos about the idea of DQN. My Code is this (mine is written in tflearn and his in keras): ...

reinforcement-learning python q-learning

asked Jun 30 '18 at 17:12

Flajt

164 bronze badges

2

votes

1answer

161 views

Dueling DQN what does a' mean?

what does $a'$ mean in the "combining" equation in Dueling DQN? (top of the page 5) $$Q(s,a; \theta, \alpha, \beta) = V(s; \theta, \beta) + \biggl( A(s, a; \theta, \alpha) - \frac{1}{N}\sum_{a'}^{N}A(...

reinforcement-learning q-learning

asked Jun 4 '18 at 9:22

Kari

8445 silver badges26 bronze badges

3

votes

1answer

550 views

Prioritized Experience Replay - why to approximate the Density Function?

I am reading about Prioritized Experience Replay, and can't understand the following: On page 4, every transition can be selected from the table with its own probability. Here is the cumulative ...

reinforcement-learning q-learning

asked May 30 '18 at 23:32

Kari

8445 silver badges26 bronze badges

1

vote

1answer

32 views

Adding a bias makes Q-learning algorithm ineffective

I've been working through the Q-Network learning example in this Arthur Juliani's blog. It's based on the pretty trivia Open Gym Frozen Lake example. It's base implementation get's about 47% success ...

tensorflow reinforcement-learning q-learning

asked May 23 '18 at 2:12

heneryville

1085 bronze badges

3

votes

1answer

42 views

Why does Q-learning use an actor model and critic model?

I'm currently reading Hands on Machine Learning with Scikit-Learn & Tensorflow, and I'm wondering why does Q-learning require an actor model and a critic model to learn? On page 465, it states: ...

deep-learning q-learning

asked May 10 '18 at 10:14

AshleyS

1334 bronze badges

2

votes

1answer

72 views

Experience Replay, must return minibatch back to Memory Bank?

During Experience Replay, we are randomly gathering a minibatch from the Memory bank. We then use the minibatch to correct our NeuralNetwork q-value function approximator. When done, should we return ...

q-learning

asked Apr 25 '18 at 2:13

Kari

8445 silver badges26 bronze badges

1

vote

0answers

131 views

Experience Replay Explain

I have read many blog articles, research papers and watched many youtube videos, but it seems it is hard to find why experience replay is efficient. I know that the experience replay stores (state, ...

deep-learning supervised-learning q-learning dqn

asked Apr 24 '18 at 17:22

Dongyu Kang

61 bronze badge

Questions tagged [q-learning]

Related Tags