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.

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98 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 ...
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56 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 ...
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88 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 ...
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194 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 ...
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39 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 ...
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74 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 ...
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43 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 ...
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24 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 ...
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48 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 ...
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35 views

Can multiple reinforcement algorithms be applied to the same system?

Can a system, for instance robotic vehicle, be controlled by more than one reinforcement learning algorithm. I intend to use one to address collision avoidance whereas the other to tackle autonomous ...
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32 views

High variance in performance of q-learning agents trained with same parameters

I am training an agent to play a simple game using double deep q learning. However, the variance in agent performance is very high, even for agents trained with same model parameters. For example, I ...
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34 views

Measure grid-world environments difference for reinforcement learning

I'd like to measure the difference between 2 grid-worlds to determine the generalization capacity of my agent using tabular Q-learning. Example (OpenAI Frozen Lake) : SFFF FHFH FFFH HFFG and : ...
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106 views

Do we need to reset the DQN network after every episode?

I was going through this implementation of Reinforcement learning where model is being trained to manage the number of bikes at a station. Here, line 78 represents the loop over all episodes (if I ...
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114 views

Reinforcement Learning with limited number of episodes

I try to implement RL to a case something like this: This game consist of several rounds. Every round the players need to generate a maze that consists of rooms. There are around 1000 different ...
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38 views

Comparison and understanding of different version of DDQN?

There are several version of DDQN floating around. Sutton gives one that is a simple symmetric random update of the two Q functions. I think other papers (Silver paper for example) use a kind of ...
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103 views

Exploration rate decay and training in Q learning

I'm trying to replicate the results of the DeepMind's paper with Breakout included in OpenAI Gym. I wonder how much frames should I keep until I reach the fixed exploration rate. Actually it reaches ...
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233 views

Training RL agent on timeseries trading data with Continous Deep Q or NAF

I am writing an MDP based agent that is supposed to learn to place bids and asks in a trading environment. The system requests 2 values (mWh energy and $, both being positive or negative). Every ...
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173 views

Help with implementing Q-learning for a feedfoward network playing a video game

I want to train a feedforward neural network to play a video game called Puyo Puyo 2, using reinforcement learning. More specifically, I'm trying Q-learning but I'm open to better alternatives. In ...
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65 views

Is it possible to prove that the target policy is better than the behavioural policy based on learned Q values?

I have retrospective data for a sort of "behaviour policy" which I will use to train a deep q network to learn a target greedy policy. After learning the Q values for this target policy, can we make ...
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32 views

Defining states and possible actions in Q learning

I am trying to define the number of states and possible actions for a reinforcement learning problem that I want to solve with Q-learning, but I am a bit confused, as I'm totally new to reinforcement ...
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33 views

CartPoleV0 model is not getting trained in even after 1500+ episodes using deep Q-learning

I am new to deep Q learning and trying to train the open AI cartpole_V0 game using deep Q learning. Here is my code: ...
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23 views

Reward firstly increase, but after more episodes, start decrease, and weights diverges

I'm making a simple deep Q learning algorithm, with cartpole-v1 env. Like you can see in chart, after many episodes the reward decrease, some possible reasons? The exploration vs axplotation algorithm ...
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12 views

DQN + HER, TD Error spiked then success rate plummets. What went wrong?

TL;DR: I trained a DQN + HER model using stable-baselines library for a custom environment. I noticed that in most runs, sometimes the TD-Error will spike and then the success rate of my model ...
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47 views

Reinforcement learning for rearranging the mobile home screen icon layout: what inputs/states do I need to pass into the algorithm?

I have a problem where I need to rearrange a particular user's mobile home screen icon layout. Let's say that the social media app usage of a user is high compared to other app usage. So I need the ...
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18 views

Training labels: integers or vectors?

I'm trying to implement Deep Q Learning using Tensorflow. The input is a vectorized representation of the state, and the output is a vector whose length is the number of possible actions. I've already ...
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13 views

Basic RL code for Production Line Optimization ( Need help )

I am working on how to optimize a Production line given 4 Workstations , 4 Employees(MA) . There are 5 products whose Workpackages need to be completed ( in order ) and these workpackages need to be ...
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39 views

MicroPython MicroMLP: How do I reward the program based on state?

I have been trying to use MicroMLP to teach a small neural network to converge to correct results. Ultimately, I want to have three outputs, one which is high priority (...
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11 views

What is an appropriate stop criteria for training on a non-stationary environment in reinforcement learning?

I'm currently studying reinforcement learning (RL) and would like to understand non-stationary environments better. So for stationary environments, the Q-values of all state-action pairs converge ...
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24 views

How DynaQ behaves in stochastic world in comparison with other reinforcement learning algorithms?

I came across of implementations of a bunch of algorithms on stochastic windy gridworld. This is the graph comparing their performance: So clearly, it seems that DynaQ performs better than all other ...
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60 views

Is there any toy example that can exemplify the performance of double Q-learning?

I recently tried to reproduce the results of double Q-learning. However, the results are not satisfying. I have also tried to compare double Q learning with Q-learning in Taxi-v3, FrozenLake without ...
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36 views
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45 views

How to use Deep Q-Network with two-dimensional input? Hands-on Machine Learning 2

I'm studying with the book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow, and I'm trying to implement the Deep Q-Network example that can be found on Github but that the input ...
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25 views

Q-learning in Python (3D grid navigation): Values do not change between episodes

I am trying to implement a Q-learning algorithm in Python for a 3D gird world as part of an assignment, wherein the environment.py file defines the actions to be taken. So far I have tried several ...
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36 views

Pytorch and keras ddqn seem identical, only keras learns

I followed a tutorial for ddqn to beat pong, it beats it with a perfect score in keras, but trying to translate it to pytorch it doesn't learn at all. What am I missing? I pasted all the code for each ...
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65 views

Strange behavior of Q-learning agent after being trained

I built a simple X*Y grid world environment to learn and then trained my agent over it. All worked fine and the agent learned as well. Let me give some detail about the environment. Environment: A ...
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2answers
87 views

How can I model this problem of delivering assets by choosing a route with reinforcement learning?

I would like to build a model based on reinforcement learning (RL) for the following scenario Recommend the best route (of cities listed for a given country) that satisfies the required criteria (...
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86 views

My Double DQN with Experience Replay produces a no-action decision most of the time. Why?

I've written a Double DQN-based stock trading bot using mainly time series stock data. The internal network of the Double DQN is a LSTM which handles the time series data. An Experience Replay buffer ...
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47 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 \...
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402 views

DQN Q-mean values converge negatively

I'm trying to implement my own DQN. So far I think my code is good, but my Q-values (I'm getting the mean of all the values for every episode) tends to converge near-zero but negatively. It is normal? ...
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22 views

How to serve a deep q network using tensorflow serving?

How to Serve a Deep Q Network using Tensorflow Serving. I have built a Deep Q Network using Multilayer Perceptron. Is it possible to serve it?
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1answer
32 views

Is is not possible to achieve average reward of more than 20-40 with simple Q-Learning

I have implemented the simple Q-Learning based solution for AI-gym's Cartpole-v0. However, despite changing hyper-parameters, and rechecking my code, I cannot get an average reward (N-running reward) ...
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1answer
548 views

Problem over DQN Algorithm not converging on snake

I'm using a DQN Algorithm to play Snake. The input of the neural network is a stack of 4 images taken from the games 80x80. The output is an array of 4 values, one for every direction. The problem ...

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