Questions tagged [q-learning]

A model-free reinforcement learning technique.

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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 ...
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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 ...
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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 ...
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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}[...
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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 ...
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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), ...
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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 ...
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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 ...
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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. ...
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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 ...
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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 ...
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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 ...
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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-...
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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 ...
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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 ...
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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 ...
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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. ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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?
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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 ...
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349 views

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

What is the difference between the DP-based algorithm and Q-learning?
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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: ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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. ...
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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') + \...
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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 ...
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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: ...
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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 ...
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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 ...
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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 ...
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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 ...
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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): ...
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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(...
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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 ...
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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 ...
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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: ...
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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 ...
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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, ...