Questions tagged [q-learning]

A model-free reinforcement learning technique.

Filter by
Sorted by
Tagged with
1
vote
0answers
10 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 ...
0
votes
0answers
15 views

What reinforcement learning algorithm to choose for self-driving car

I have a car that has three sensors at the front. Using these sensors only I want to let it learn to drive on a track. I'm new to reinforcement learning, but I was thinking about using the Q-learning ...
0
votes
0answers
24 views

Applying Reinforcement Learning in the following scenario

I'm working on a scenario/environment where I have a simulation that provides an arrangement or results of the simulation that has data in a format of samples in vectors(x,y,z,N). Let's say it maps ...
0
votes
0answers
26 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 ...
0
votes
0answers
50 views

Prioritized Experience Replay - for whole episodes

I want to use Prioritized Experience Replay for whole episodes, instead for single transitions. What's the best way to define the priorities - as episodes can be of different lengths? Personally I ...
1
vote
1answer
24 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 ...
1
vote
0answers
25 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 ...
1
vote
1answer
40 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 ...
0
votes
1answer
41 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 ...
0
votes
0answers
14 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 ...
1
vote
1answer
15 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}[...
1
vote
1answer
93 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 ...
3
votes
1answer
142 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), ...
1
vote
1answer
122 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 ...
1
vote
2answers
116 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. ...
1
vote
1answer
244 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 ...
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 ...
2
votes
0answers
71 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 ...
0
votes
0answers
17 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-...
1
vote
0answers
48 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 ...
2
votes
1answer
23 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 ...
2
votes
1answer
30 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 ...
0
votes
0answers
52 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. ...
0
votes
1answer
253 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 ...
0
votes
0answers
22 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 ...
2
votes
1answer
341 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 ...
0
votes
0answers
15 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 ...
0
votes
1answer
54 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 ...
1
vote
0answers
11 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 ...
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 ...
0
votes
1answer
46 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 ...
0
votes
0answers
11 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?
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 ...
2
votes
1answer
672 views

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

What is the difference between the DP-based algorithm and Q-learning?
0
votes
1answer
63 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: ...
0
votes
1answer
48 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 ...
1
vote
1answer
68 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 ...
1
vote
1answer
251 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 ...
1
vote
1answer
1k 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 ...
1
vote
1answer
50 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 ...
1
vote
0answers
91 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 ...
1
vote
1answer
71 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. ...
1
vote
1answer
227 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') + \...
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 ...
2
votes
1answer
3k 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: ...
2
votes
1answer
63 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 ...
1
vote
0answers
205 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 ...
6
votes
2answers
111 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 ...
2
votes
1answer
89 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 ...
1
vote
1answer
71 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): ...