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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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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1answer
356 views

How do we compute the target value when the agent ends up in the terminal state?

I am working on a deep reinforcement learning problem. Throughout the episode, there is a small positive and negative reward for good or bad decisions. In the end, there is a huge reward for the ...
3
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1answer
697 views

Why is my implementation of Q-learning not converging to the right values in the FrozenLake environment?

I am trying to learn tabular Q learning by using a table of states and actions (i.e. no neural networks). I was trying it out on the FrozenLake environment. It's a very simple environment, where the ...
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0answers
23 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 ...
2
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1answer
97 views

When training a DQN, how should we update the value of actions that were not taken?

Let's say that we have three actions. The highest-valued action of the three choices is the first. When training the DQN, what do we do with the other two, as we don't have a target for them, since ...
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2answers
730 views

What does the symbol $\mathbb E$ mean in these equations?

I came across some papers that use $\mathbb E$ in equations, in particular, this paper: https://arxiv.org/pdf/1511.06581.pdf. Here is some equations from the paper that uses it: $Q^\pi \left(s,a \...
5
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1answer
123 views

Can exogenous variables be state features in reinforcement learning?

I have a question about state representation of Q-learning or DQN algorithm. I'm still a beginner of RL, so I'm not sure that is it suitable to take exogenous variables as state features. For example,...
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3answers
540 views

Reinforcement learning: How to deal with illegal actions? [duplicate]

I'm a beginner of RL and currently trying to make DQN agent that can act optimally in a simple situation. In the situation agent should decide at what rate to charge or discharge the electrical ...
4
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1answer
185 views

Is tabular Q-learning considered interpretable?

I am working on a research project in a domain where other related works have always resorted to deep Q-learning. The motivation of my research stems from the fact that the domain has an inherent ...
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1answer
87 views

Deep Q Learning for Simple Game Not Effective

This is a follow-up question about one I asked earlier. The first question is here. Basically, I have a game where a paddle moves left and right to catch as much "food" as possible. Some food is good (...
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1answer
110 views

Deep Q Learning Algorithm for Simple Python Game makes player stuck

I made a simple Python game. A screenshot is below: Basically, a paddle moves left and right catching particles. Some make you lose points while others make you gains points. This is my first Deep Q ...
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0answers
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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1answer
57 views

Will the target network, which is less trained than the normal network, output inferior estimates?

I'm having some trouble understanding some parts of the usage of target networks. I get that having the same network predict the state/action/advantage values for both the current networks can lead ...
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0answers
90 views

How can I use Q-learning for inventory decision making?

I am trying to model operational decisions in inventory control. The control policy is base stock with a fixed stock level of $S$. That is replenishment orders are placed for every demand arrival to ...
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1answer
114 views

Probabilistic action selection in pursuit algorithm

In the Pursuit algorithm (to balance exploration and exploitation), the greedy action has a probability say $p_1$ (updated every episode) of being selected, while the rest have a probability $p_2$ (...
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1answer
133 views

Is it possible to have a dynamic $Q$-function?

I am trying to use Q-learning for energy optimization. I only wish to have states that will be visited by the learning agent, and, for each state, I have a function that generates possible actions, so ...
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0answers
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 ...
2
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1answer
48 views

If deep Q learning involves adjusting the value function for a specific policy, then how do I choose the right policy?

I wrote a simple implementation of Flappy Bird in Python, and now I'm trying to train an agent to play it at a reasonable skill level using TFLearn. I feed the network an input vector of size 4: ...
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0answers
91 views

How does Friend-or-Foe Q-learning intuitively work?

I read about Q-Learning and was reading about multi-agent environments. I tried to read the paper Friend-or-Foe Q-learning, but could not understand anything, except for a very vague idea. What does ...
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1answer
609 views

Does AlphaZero use Q-Learning?

I was reading the AlphaZero paper Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, and it seems they don't mention Q-Learning anywhere. So does AZ use Q-...
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2answers
204 views

What is the difference between return and expected return?

At a time step $t$, for a state $S_{t}$, the return is defined as the discounted cumulative reward from that time step $t$. If an agent is following a policy (which in itself is a probability ...
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2answers
114 views

How to stop evaluation phase in reinforcement learning with epsilon-greedy Monte Carlo agent?

I have implemented an epsilon-greedy Monte Carlo reinforcement learning agent like suggested in Sutton and Barto's RL book (page 101). As far as I understood epsilon-greedy agents so far, the ...
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1answer
531 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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1answer
436 views

Deep Reinforcement Learning: Rewards suddenly dip down

I am working on a deep reinforcement learning problem. The policy network has the same architecture as the one Deepmind published in 'Playing Atari with Deep Reinforcement Learning'. I am also using ...
3
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1answer
47 views

How do I represent a multi-dimensional state using a neural network?

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 values in the set of 15 cards, where the card value is a prime number ...
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1answer
111 views

Q-learning, am I interpreting correctly $Q(s,a) = r + \gamma \max_{a'} Q(s',a')$?

Ok, due to previous question I was pointed to use reinfrocement learning. So far what I understood from random websites is the following: there is a Q(s,a) function involved I can assume my neural ...
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1answer
608 views

Choice of inputs features for Snake game

I am designing a neural network using Deep Q-Learning, which teaches an agent how to play Snake (The classic Nokia game from the 90'ies). The goal of the game is to navigate the snake on a playing ...
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0answers
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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1answer
676 views

Static or dynamic learning rate (Q-learning)

I have the following code (below), where an agent uses Q-learning (RL) to play a simple game. What appears to be questionable for me in that code is the fixed learning rate. When it's set low, it's ...
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2answers
4k views

Can Q-learning be used for continuous (state or action) spaces?

Many examples work with a table-based method for Q-learning. This may be suitable for a discrete state (observation) or action space, like a robot in a grid world, but is there a way to use Q-learning ...
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0answers
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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1answer
118 views

Experience Replay Not Always Giving Better Results

I have recently started working on a control problem using a Deep Q Network as proposed by DeepMind (https://arxiv.org/abs/1312.5602). Initially, I implemented it without Experience Replay. The ...
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0answers
75 views

Why is there inconsistency in the definitions of the retrace?

In Section 4.3 of paper Learning by Playing - Solving Sparse Reward Tasks from Scratch, the authors define Retrace as $$ Q^{ret}=\sum_{j=i}^\infty\left(\gamma^{j-i}\prod_{k=i}^jc_k\right)[r(s_j,a_j)+\...
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1answer
92 views

Picking a random move in exploitation in Q-Learning

I've been unsure about a principle of Q-Learning, I was hoping someone could clear it up. When a new state is encountered, and thus there are no existing Q values, and that the algorithm decides to ...
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4answers
721 views

How to stop DQN Q function from increasing during learning?

Following the DQN algorithm with experience replay: Store transition $\left(\phi_{t}, a_{t}, r_{t}, \phi_{t+1}\right)$ in $D$ Sample random minibatch of transitions $\left(\phi_{j}, a_{j}, r_{j}, \...
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1answer
60 views

Encourage Deep Q to seek short-term reward

I understand that gamma is an important factor in determining the rewards for a deep Q agent, however during testing of my network I am noticing that the agent is outputting more actions to "do ...
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1answer
61 views

Maximum Q value for new state in Q-Learning never exists

I'm working on implementing a Q-Learning algorithm for a 2 player board game. I encountered what I think may be a problem. When it comes time to update the Q value with the Bellman equation (above), ...
3
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0answers
51 views

Deep Q-Learning agent poor performing actions. Need help optimizing

I'm trying to make deep q-learning agent from https://keon.io/deep-q-learning My environment looks like this: https://imgur.com/a/OnbiCtV As you can see my agent is a circle and there is one gray ...
6
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2answers
581 views

Can DQN perform better than Double DQN?

I'm training both DQN and double DQN in the same environment, but DQN performs significantly better than double DQN. As I've seen in the double DQN paper, double DQN should perform better than DQN. Am ...
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2answers
243 views

Can gamma be greater than 1 in a DQN?

If I have a DQN, and I care A LOT about future rewards (moreso than current rewards), can I set gamma to a number greater than 1? Like 1.1 perhaps?
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3answers
2k views

Why doesn't Q-learning converge when using function approximation?

The tabular Q-learning algorithm is guaranteed to find the optimal $Q$ function, $Q^*$, provided the following conditions (the Robbins-Monro conditions) regarding the learning rate are satisfied $\...
6
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1answer
2k views

Can Q-learning be used in a POMDP?

Can Q-learning (and SARSA) be directly used in a Partially Observable Markov Decision Process (POMDP)? If not, why not? My intuition is that the policies learned will be terrible because of partial ...
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1answer
91 views

DQN Q-values are static

I am working on a DDQN with 5 LSTM layers and 3 actions as output and state space of 21 features. I am dividing the dataset into episodes of 720 timesteps, for each episode the agent acts greedily for ...
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0answers
390 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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1answer
77 views

How to build a DQN agent which can be trained through interactive learning?

I am trying to create a chatbot whose dialogue policy model will be trained through reinforcement learning. Dialogue Policy is responsible for selecting the action to take based on the given state of ...
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0answers
104 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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0answers
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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1answer
130 views

Why Q2 is a more or less independant estimate in Twin Delayed DDPG (TD3)?

Twin Delayed Deep Deterministic (TD3) policy gradient is inspired by both double Q-learning and double DQN. In double Q-learning, I understand that Q1 and Q2 are independent because they are trained ...
3
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1answer
530 views

What are the differences between the DQN variants?

There are several variants of the DQN model. For example, double DQN, duelling DQN, prioritized DQN, distributed prioritized DQN, episodic memory DQN, asynchronous n-step DQN and multiple DQN. What ...
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2answers
88 views

Why is the max a non-expansive operator?

In certain reinforcement learning (RL) proofs, the operators involved are assumed to be non-expansive. For example, on page 6 of the paper Generalized Markov Decision Processes: Dynamic-programming ...

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