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Questions tagged [q-learning]

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

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31 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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1answer
18 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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1answer
23 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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1answer
22 views

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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22 views

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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1answer
44 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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1answer
16 views

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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25 views

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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40 views

Initialize replay memory and action value function Q

I am not sure I can ask that question here, but I will try an attempt. I am trying to implement Beat Atari with Deep Reinforcement Learning. They explained very well each steps, but they ask you to ...
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26 views

Why isn't my reinforcement learning agent learning anything useful?

I've been trying to implement a Q-learning agent to play the game of snake. There are many examples of deep Q-learning agents doing this on github but I couldn't find any with simple Q-learning and as ...
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67 views

How to apply Reinforcement Learning to time-series sampling?

I want to apply the concept of Reinforcement Learning (RL) to help an agent decide when to sample an unobserved time-series signal. Can you help/guide me on how to proceed? The problem setup can be ...
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1answer
46 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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16 views

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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2answers
155 views

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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2answers
76 views

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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46 views

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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1answer
46 views

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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1answer
159 views

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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277 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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22 views

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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2answers
316 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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2answers
31 views

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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1answer
49 views

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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1answer
97 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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1answer
45 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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11 views

Features Observability In Sequential Data For Deep Network

I have an unconventional classification problem over sequential data (at least to me since I am relatively new to this area). The problem goes as follows. For time index $i$ we observe features $F_i$ ...
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2answers
54 views

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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1answer
69 views

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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1answer
278 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 ...
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1answer
135 views

Is double Q-learning redundant when using target networks?

Generally speaking, the purpose behind target networks is to reduce the impact of current changes on the model. i.e. if I performed action a and got some reward <...
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1answer
60 views

How can Deep Q Learning be applied to scenarios with rewards only received in a final step?

I am applying DQ Learning to a continuous action space with rewards received at the end of each trial. My agent is in a fixed 24step long setting where it receives the reward at the end of those 24 ...
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1answer
102 views

Experience replay, why store SARS' and not SAQRS'

For Q-learning Experience replay, why do we store into the bank observations: { stateFrom, actionIx, imediateReward, resultingState } instead of { stateFrom, actionIx, actionQValue, ...
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1answer
114 views

Why semi-gradient is used instead of the true gradient in Q-learning?

I am asking a duplicated question that nobody has answered yet. In Q-learning with function approximation, the objective is to minimize MSE between the target $r + \gamma \max_{a'} Q(s',a',w)$ and the ...
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1answer
20 views

Q-learning with slow rewards [closed]

A team in my company has implemented a basic model-free Q-Learning agent in relation to inventory control. The problem (in my eyes) is that it only knows its reward once per day based on revenue gain ...
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2answers
3k views

Why was the letter Q chosen in Q-learning?

Why the letter Q was chosen in the name of Q-learning? Most letters are chosen as an abbreviation, such as $\pi$ standing for policy and $v$ stands for value. But I don't think Q is an abbreviation ...
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1answer
70 views

Q-Learning: state independent of agent's action [closed]

Could state be independent of the action chosen by agent? We would have a situation in which agent learns only which actions are the best in specific states without having any impact on those states (...
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0answers
243 views

Is Monte Carlo Tree Search policy or value iteration (or something else)?

I am taking a Reinforcement Learning class and I didn’t understand how to combine the concepts of policy iteration/value iteration with Monte Carlo (and also TD/SARSA/Q-learning). In the table below, ...
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1answer
27 views

Optimality in Hierarchy of Machines (HAM Framework)

How is it that the HAM framework provides the Hierarchically Optimal solution while the MAXQ framework provides the recursively optimal solution? The above statement is based on Section ...
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0answers
13 views

Why the invariant reward helps training?

I am new to Machine Learning, and I am trying to solve MountainCar-v0 using Q-learning. I can solve the problem now, but I am still confused. According to the MountainCar-v0's Wiki, the reward ...
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1answer
53 views

In MDPs with deterministic actions, should I use Q-learning or TD(0)?

Suppose in an Markov Decision Process (MDP), we have transition $(s, a, r, s', a', r', s'', ...)$, learning rate $\alpha$ and discount factor $\lambda$. The update formula of $TD(0)$: $V(s) \...
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1answer
65 views

Why Deep Qlearning is better than Qlearning? [closed]

If Q-learning is supposed to converge toward the optimal policy, how is it possible to do better?
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1answer
235 views

Does higher maximum Q value imply better or worse performance?

Suppose I run a RL algorithm and for every episode, I grab the (average) maximum Q value. I do this for several runs (with different hyper-paramters, for instance) to compare performance of my RL ...
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2answers
607 views

How to perform deep Q-learning batch update step on a neural network with multiple outputs

I am taking on deep Q-learning and I am stuck at understanding one particular thing. I have googled multiple deep Q-learning examples, but literally everyone posting tutorials uses a cart-pole game to ...
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3answers
1k views

Why don't we use importance sampling for one step Q-learning?

Why don't we use importance sampling for 1-step Q-learning? Q-learning is off-policy which means that we generate samples with a different policy than we try to optimize. Thus it should be ...
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1answer
156 views

How can I minimize future long-term reward in deep Q-learning? [closed]

I’m trying to implement deep Q-learning on a problem were the rewards the agent receives are errors from another model. The RL agents job is to minimize the long-term reward (error) instead of ...
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1answer
272 views

Confused about Function Approximation for Q Learning

I am not sure that I understood Funtion approximation for Q Learning. So basicall with FA we don't use tables anymore? Each state is now represented with features, and we multiply those features with ...
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2answers
554 views

Is planning in Dyna-Q a form of experience replay?

In Richard Sutton's book on RL (2nd edition), he presents the Dyna-Q algorithm, which combines planning and learning. In the planning part of the algorithm, the Dyna-agent randomly samples n state-...
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1answer
224 views

Parameterizing Reinforcement Learning card game state space

I want to model a particular card game as a reinforcement learning problem. For simplicity let's say it is a single standard 52 card deck, and let's say it is just 2 players. The exact details are not ...
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26 views

Possible Learning Approach

I am working on a reinforcement learning problem where I have 8 different actions [1 2 3 4 5 6 7 8] and a symmetric state matrix of 10 by 10 (only upper or triangular matrix is sufficient). I know I ...
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1answer
1k views

Overview over Reinforcement Learning Algorithms

I'm currently searching for an Overview over Reinforcement Learning Algorithms and maybe a classification of them. But next to Sarsa and Q-Learning + Deep Q-Learning I can't really find any popular ...