Questions tagged [q-learning]

A popular reinforcement learning algorithm, an instance of TD (temporal difference) learning.

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Instability in Multi-Agent Reinforcement Learning

I've got a program tackling a Multi-Agent Markov Decision Process using Deep Reinforcement Learning. To solve the problem I've tried a few algorithms broadly belonging to these two classes: A2C (...
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Can an agent be trained in a completely random environment (the rules and actions stay the same)

I am specifically talking about the FrozenLake example from openAI gym to illustrate my question. https://gym.openai.com/envs/FrozenLake-v0/ The way I understand how Q-Learning or DQL works on the ...
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Consider more than just a single state-action pair before giving a reward in deep q learning?

TLDR If the environment is rather complex (momentum, velocity, ...), but the action space is rather easy (left, right), is it not better to take multiple decisions into account before giving a reward ...
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Q-learning update formula

I am a beginner and it is my first question. I know that Q-learning update equation is: $Q(s_t, a_t) = Q(s_t, a_t)+α(r_{t+1} +γ·max_AQ(s_{t+1}, a_t)−Q(s_t, a_t))$ But in some of the researches it is ...
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Q-Learning [Sutton]: why random variable in formula

Sutton et al. use throughout their book Reinforcement Learning capital letters to describe random variables. At page 131 they introduce Q-Learning. $Q(S_t,A_t)\leftarrow Q(S_t,A_t) + \alpha [R_{t+1} ...
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Reinforcement Learning by Sutton: Episodic and Continuing Tasks

Referred to Reinforcement Learning by Sutton: What are Episodic and Continuing Tasks? In my opinion: $\textbf{Episodic Tasks}$ A task is episodic, if there exists a final time step $T$ so that the ...
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Model-based Reinforcement Learning: Calculate expected Reward

I'm very new to reinforcement learning and I try to understand the concept of it. In modelbased learning, there is a known probability distribution of the rewards and of the transitions. So let's say ...
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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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46 views

Deep Q Learning best practice

I'm new in deep q-learning and I have understood the main concepts of it and I'm trying to solve problems with DQL. The problem is that I don't know how to initialize some key values of the algorithm ...
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Small difference of q-function between different actions for the same state

I am trying out reinforcement learning using Q-learning. The data come from some made-up equations so I have infinite number of data. One thing that troubles me is after I learn the Q-function, I use ...
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DQN - agent doesn't improve policy

I have a simple grid environment. The player is in the upper left corner and it's goal is to get to lower right corner. The player receives +0.2 points for moving in the direction of the goal, -0.2 ...
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149 views

Delayed Rewards in Reinforcement Learning

I have an MDP where the rewards are delayed for six steps as follows: The reward from action at time t is received when the action at time t+6 is taken. The reward from action at time t+1 is ...
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How to use data from other policies in order to find optimal policy in model-free rl?

I am struggling to understand whether experience from one policy can be used to find optimal policy. Suppose that I have gathered many data (state, action, reward, next_state) by following random ...
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201 views

Q-learning agent stucks in an infinite loop

I am simulating a mouse to find a cheese on an empty table. I randomly put a cheese on the table and let the mouse find the cheese without falling off the table. The problem is, in test part, agent ...
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33 views

Rationale behind Q-learning

I am reading Sutton Barto on Reinforcement Learning. I understand that $TD(\lambda)$ methods propose better performance than Monte Carlo methods, with TD methods combining advantages of Dynamic ...
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165 views

Reward attribution in deep q learning and texas holdem poker

I’m having issues with reward attribution in poker using deep q learning. Multiple actions will yield one reward, but the reward is only known at the end of the hand, not before. I have built a gym ...
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25 views

Why not sample action from Q values?

When collecting experience from which to estimate a Q(s,a) function, one common technique in the literature is to follow an epsilon greedy-strategy. In this strategy, the agent selects a random action ...
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134 views

How does the Dyna Q algorithm works?

I'm having a hard time trying to understand how the dyna Q algorithm works. I put the picture which helps me to understand. My questions are: What planning really means? (it's the (f) in this picture)...
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273 views

Episodic Semi-gradient Q-learning for Estimating approximation of optimal action-value function

at page 244 of Sutton and Barto book on Reinforcement Learning (book) is described the pseudocode for episodic semi-gradient Sarsa, while it is never given a pseudocode for the corresponding episodic ...
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279 views

DQN - How to feed the input of 4 still frames from a game as one single state input

I was reading this blog about Deep Q-Learning. 1- In the The input section of the blog, I wanted to know how do we feed the 4 still-frames/screenshots from the game, that represent the input state, ...
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113 views

DQN - breaking correlation between consecutive samples and random sampling

I was reading through some blogs about Deep Q-Learning (DQN), and I have 2 questions: 1- I didn't understand how breaking the correlation between consecutive samples (i.e. train the network with ...
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Is there a branch of machine learning that can deal with near infinite state spaces

So I have a game type problem defined as follows; Up to 10 players Each player has: 64 tiles 200 piece types Up to 20 pieces in play at any time There's a random ...
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47 views

How do Tile Coding offsets still cover full state space / affect edge cases?

Reading Sutton & Barto I’m having a hard time visualizing the implementation of the tile coding discretization of states. Specifically, if tilings are offset, how does this effect edge cases? For ...
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Different algorithms categorized in reinforcement learning

For some time I am going through reinforcement learning, and have found a lot of diverse information specially in area of Policies (algorithms). I figured out that policies can be classified in On ...
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339 views

Differences between Sarsa and Q-learning control procedural algorithms

I am referring to pages 130-131 of Sutton and Barto book on Reinforcement Learning available here: book I don't understand the slight difference that there is between the two procedural algorithms ...
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437 views

Limits and constraints for Q-learning

I have simple implementation of Q-learning algorithm and I'm trying to run it on States space size = 36865 Actions space size = 25 So my resulting Q-table is ...
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266 views

Q-learning shows worse results than value iteration

I'm trying to solve the same problem with different algorithms (Travel max possible distance with a car). While using value iteration and policy iteration I was able to get the best results possible ...
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Reinforcement learning based Q-learning for wireless routing

In the Q-learning method to get the optimal strategy, the update method is like the following: \begin{equation} Q(S,A) \leftarrow \ Q(S,A) + \alpha [R+\gamma~max_a(Q(s',a)) -Q(S,A)] \end{equation} If ...
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Vanishing reward function in Q-Learning

Imagine that the agent receives a positive reward upon reaching a state $s$. Once the state $s$ has been reached the positive reward associated with it vanishes and appears somewhere else in the state ...
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486 views

How to explain and visualize a Q Learning Agent?

What are some common visualization approaches used in explaining the behavior of a Q-Learning agent? Here is an excerpt of some example Q values for 5 actions serialized to json: ...
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How does DQN parameter updates work in simulation?

I've already read almost every Questions-answers and material related to DQN, deep reinforcement learning, but I'm struggling to start working on simulation. First of all, I'm trying to code using ...
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Deep Q - Learning Exploration - BestQ Value

I am trying to implement a Deep Q - Network to play Asteroids. Unfortunately, I am not sure how to calculate the Q Value exactly if I am exploring. For example, the Agent is exploring for 1 second (...
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Initialize replay memory and action value function Q

I am not sure I can ask this question here, but I will make an attempt. I am trying to implement Beat Atari with Deep Reinforcement Learning. They explained all steps very well, but they ask you to ...
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198 views

How to apply multi agent deep reinforcement learning to an environment with discrete action space

Do you know or have heard about any cutting edge deep reinforcement-learning algorithm which can be successfully applied for discrete action-spaces in multi-agent settings? I have been researching ...
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Convergence criterion for R-learning algorithm

I'm trying to find a policy for a simple game using R-learning algorithm. I have a field with values (agent can move in 4 directions) and the goal is to get from starting point to finish point with ...
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Value iteration does not converge when using Q learning

I have a simple game and want my agent to play it with a help of reinforcement learning. We have a board and a value in each cell. The goal is to go from start to finish point with the highest score (...
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Why in $Q$-Learning, policy $\pi$ is evaluated through another policy $u$?

I've been watching David Silver's courses about Reinforcement Learning. According to his lectures, policy $\pi$ is evaluated by evaluating another policy $\mu$. But I cannot understand: why is it so? ...
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Adding additional constrains to OpenAi Gym

I'm currently working trough some examples which should finally end in a DQN Reinforcement Learning for the CartPole example in the openAI-Gym. Copied some code from GitHub which isn't deep yet: <...
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Understanding Q-learning for continuous actions

I am reading the paper on Normalized Advantage Functions for continuous Q-learning and I am having trouble understanding why the advantage function takes this particular form: Why is the Advantage ...
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Why is this the correct formula to update the NN weights in Q-learning?

I'm trying to implement Q-learning to train an AI bot to play Pokemon battles. Since there is a large state space (corresponding to all possible states a battle can have in between moves), I can't use ...
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651 views

SARSA with Linear Function Approximation weight overflow

I'm trying to solve the CartPole problem, implemented in OpenAI Gym. In each state the agent is able to perform one of 2 actions move left or right. The reward is always +1. The epsiode ends after 500 ...
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Neural network how to deal with comparison

I'm currently working on a DQN network and this question comes to me. As far as I know, neural networks are good at dealing with values that have never seen (generalisation). E.g. If a classification ...
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847 views

Why update SARSA with S'A' at all if the goal is a less aggressive exploitation policy?

Why is it that we update the Q values using S' and A' and not the maximum as in Q-learning? If the goal is to have a less aggressive exploitation policy, why don't we simply use an epsilon greedy ...
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Q-learning with 100-500 discrete actions

I've a Reinforcement Learning problem where I want to learn the Q function. For action space of size in the order of 100s is Q learning a good option? Will it converge?
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What is momentum-like training aid technique for reinforcement learning (Q learning)?

Is there any method that could help a reinforcement learning (specifically Q learning) model converge? Can optimization strategy like momentum/Adam/RMSProp applied to Q learning to update Q-table ...
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193 views

Q learning: overtraining and converagence

I'm working on a Q learning model to autopilot Flappy Bird (follow http://sarvagyavaish.github.io/FlappyBirdRL/): it manage to reach a good score like 500 after a while of training: But after longer ...
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190 views

What could be the causes of weights not changing during backpropagation?

I'm currently working on a Dueling-Double DQN model, and I noticed that though the loss (mse of Q values between training and target networks) seems to be decreasing, the distribution of weights in ...
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Deep Q-Learning: Experience replay overriding old Memories?

This is my first question on SE in general. So if I make any mistakes - please feel free to point them out to me. My Question is about Deep Q-Learning. I've been working into some code examples and ...
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Reward function for grid based path planning deep Q-learning agent

I'm really getting stuck on creating a good reward function for my agent and could use some advice. I'll explain the setting for my question first: Agent Description The agent in question exists in ...
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650 views

Is Deep-Q Learning inherently unstable

I'm reading Barto and Sutton's Reinforcement Learning and in it (chapter 11) they present the "deadly triad": Function approximation Bootstrapping Off-policy training And they state that an ...