Questions tagged [dqn]

For questions related to the deep Q-network (DQN), which is a deep neural network (e.g. a convolutional neural network) trained with a variant of Q-learning. The expression was coined in the paper "Playing Atari with Deep Reinforcement Learning" (2013) by Google's DeepMind.

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What's the best way to take a list of lists as DQN input?

I have my own environment for the DQN algorithm. In my environment, the state space is represented by a list of lists, where each sublist can be of different lengths. In my case, the length of the ...
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20 views

$\epsilon$-greedy policy in environments where actions are performed in a long term. Does it has influence?

I'm working in an environment where once an action $a \in A$ is performed, it must hold this action selection for a while. To clarify this, assumes a horizon length $h$ and the set of actions: $\{a_{1}...
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43 views

Can I train a DQN on the same dataset for multiple epochs?

I am trying to learn about reinforcement learning and chose the stock market to experiment with. I have minute by minute historical data on a particular stock for the past 20 years. I am using a ...
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1answer
60 views

Why do I get the best policy before Q values converge using DQN?

I have implemented DQN algorithm and wonder why during testing, the best performance is achieved by a policy from about 300 episode, when mean Q values converge at about 800 episode? Mean Q-values ...
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1answer
40 views

What should the input and output of the Q-network be in the case of an ordinal action space?

I recently started looking into implementations of the DQN algorithm (e.g. TensorFlow) in some more detail. All the implementations that I found use a network that gives an output for each possible ...
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92 views

What is the target output for updating a Deep Q Network

I'm trying to implement Deep Q-Learning for a pet problem having a continuous state space and discretized action space. The algorithm for table-based Q-Learning updates a single entry of the Q table - ...
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1answer
50 views

Can the rewards be matrices when using DQN?

I have a basic question. I'm working towards developing a reward function for my DQN. I'd like to train an RL agent to edit pixels on an image. I understand that convolutions are ideal for working ...
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823 views

What is the difference between Q-learning, Deep Q-learning and Deep Q-network?

Q-learning uses a table to store all state-action pairs. Q-learning is a model-free RL algorithm, so how could there be the one called Deep Q-learning, as deep means using DNN; or maybe the state-...
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56 views

Does DQN generalise to unseen states in the case of discrete state-spaces?

In my understanding, DQN is useful because it utilises a neural network as a q-value function approximator, which, after the training, can generalise to unseen states. I understand how that would work ...
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Where does this variation of the importance sampling weight come from?

I have seeing a variation in importance sampling (IS) in Prioritized Experience Replay (PER) in some implementations regarding the original paper approach stated as (in section 3.4): $$ w_{i}=\left(\...
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Is better to reward short- or long-term progress in Q-learning?

I have been training some kind of agent to reach a target using a Q-learning based approach, and I have tried two different types of rewards: Long-term reward: $\mathrm{reward} = - \mathrm{distance}(\...
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52 views

When calculating the cost in deep Q-learning, do we use both the input and target states?

I just finished Andrew Ngs's deep learning specialization, but RL was not covered, so I don't know the basics of RL. So, I have been having trouble understanding the cost function in deep Q-learning. ...
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127 views

Why does regular Q-learning (and DQN) overestimate the Q values?

The motivation for the introduction of double DQN (and double Q-learning) is that the regular Q-learning (or DQN) can overestimate the Q value, but is there a brief explanation as to why it is ...
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Can DQN outperform DoubleDQN?

I found a similar post about this issue, but unfortunately I did not find a proper answer. Are there any references where DQN is better than DoubleDQN, that is DoubleDQN does not improve DQN ?
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How to use DQN when the action space can be different at different time steps?

I would like to employ DQN to solve a constrained MDP problem. The problem has constraints on action space. At different time steps till the end, the available actions are different. It has different ...
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1answer
58 views

How can I model a problem as an MDP if the agent does not follow the successive order of states?

In my problem, the agent does not follow the successive order of states, but selects with $\epsilon$-greedy the best pair (state, action) from a priority queue. More specifically, when my agent goes ...
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DDQN Agent in Othello (Reversi) game struggle to learn

This is my first question on this forum and I would like to welcome everyone. I am trying to implement DDQN Agent playing Othello (Reversi) game. I have tried multiple things but the agent which seems ...
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1answer
49 views

How to build a Neural Network to approximate the Q-function?

I am learning reinforcement learning with Q-learning using online resources, like blog posts, youtube videos, and books. At this point, I have learned the underpinning concepts of reinforcement ...
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Would it make sense to share the layers (except the last one) of the neural networks in Double DQN?

Context: Double Q-learning was introduced to prevent the maximization bias from q-learning. Instead of learning a single Q-network, we can learn two (or in general $K > 1$) and our Q-estimate would ...
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83 views

How is the DQN loss derived from (or theoretically motivated by) the Bellman equation, and how is it related to the Q-learning update?

I'm doing a project on Reinforcement Learning. I programmed an agent that uses DDQN. There are a lot of tutorials on that, so the code implementation was not that hard. However, I have problems ...
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71 views

How to build a DQN agent with state and action being arrays?

I have a Reinforcement-Learning environment where the state is an array of 0s and 1s with length equals to the number of users the agent must satisfy (11 users). The agent must choose one of 12 ...
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How should I build this DQN agent?

I have a set of users that can be one of 3 types. They will randomly request a service from the UAV which is a drone used as a Base Station. The UAV (the agent) is tasked with allocating subchannels (...
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DQN fails to learn useful policy for the Taxi environment (Dietterich 200)

I'm building an agent to solve the Taxi environment. I've seen this problem solved with Q-Learning algorithms but my DQN consistently fails to learn anything. The environment has a discrete ...
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How should I change the hyper-parameters of the C51 algorithm, in order to obtain higher reward?

I have a scenario where, in an ideal situation, the greedy approach is the best, but when non-idealities are introduced which can be learned, DQN starts doing better. So, after checking what DQN ...
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1answer
79 views

What kind of problems is DQN algorithm good and bad for?

I know this is a general question, but I'm just looking for intuition. What are the characteristics of problems (in terms of state-space, action-space, environment, or anything else you can think of) ...
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Is Deep SARSA learning a feasible approach?

I noticed that SARSA has been rarely used in the deep RL setting. Usually, the training for DQN is done off-policy. I think one of the major reasons for this is due to greater sample efficiency in ...
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37 views

In reinforcement learning, is it possible to make some actions more likely?

In a general DQN framework, if I have an idea of some actions being better than some other actions, is it possible to make the agent select the better actions more often ?
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32 views

What adapts an algorithm to continuous or to discrete action spaces?

Some RL algorithms can only be used for environments with continuous action spaces (e.g TD3, SAC), while others only for discrete action spaces (DQN), and some for both REINFORCE and other policy ...
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1answer
89 views

Keras DQN Model with Multiple Inputs and Multiple Outputs [closed]

I am trying to create a DQN agent where I have 2 inputs: the agent's position and a matrix of 0s and 1s. The output is composed of the agent's new chosen position, a matrix of 0s and 1s (different ...
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28 views

What are the implications of storing the alternative situation (that could have been experienced) in the replay buffer?

Consider an environment where there are 2 outcomes (e.g. dead and alive) and a discrete set of actions. For example, a game where the agent has 2 guns $A$ and $B$ to shoot a monster (the monster dies ...
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1answer
41 views

Initialising DQN with weights from imitation learning rather than policy gradient network

In AlphaGo, the authors initialised a policy gradient network with weights trained from imitation learning. I believe this gives it a very good starting policy for the policy gradient network. the ...
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21 views

DQN rgb input channels problem using pytorch

I've been trying to learn about CNN's and reinforcement learning and I found this project to play with: https://github.com/adityajn105/flappy-bird-deep-q-learning I've been trying to change the code ...
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1answer
49 views

Why do we minimise the loss between the target Q values and 'local' Q values?

I have a question regarding the loss function of target networks and current (online) networks. I understand the action value function. What I am unsure about is why we seek to minimise the loss ...
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43 views

Off-policy full-random training in easy-to-explore environment

Let say we are in an environment where a random agent can easily explore all the states of an environment (for example: tic-tac-toe). In those environments, using off-policy algorithm, is it a good ...
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REINFORCE Agent suddenly drops. How to verify if it's due to catastrophic forgetting?

I am using the default implementations of REINFORCE, DQN and c51 available from the tf.agents repo (links). As you can see, DQN manages to improve performance while REINFORCE seems to suffer from ...
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156 views

Why not use the target network in DQN as the predictor after training

Target network in DQN is known to make the network more stable, and the loss is like "how good I'm now compared to using the target". What I don't understand is, if the target network is the ...
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28 views

Should the exploration rate be updated at the end of the episode or at every step?

My agent uses an $\epsilon$-greedy strategy to learn. The exploration rate (i.e. $\epsilon$) decays throughout the training. I've seen examples where people update $\epsilon$ every time an action is ...
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48 views

How does one stack multiple observations in the input layer of a convolutional neural network?

The paper, Deep Recurrent Q-Learning for Partially Observable MDPs, talks about stacking multiple observations in the input of a convolutional neural network. How does this exactly work? Do the ...
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1answer
65 views

Improving DQN with fluctuations

Hello :) I'm pretty new to this community, so let me know if I posted anything incorrectly and I'll try to change it. I'm working on the project which aim is to create self-driving agent in CARLA. I ...
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2answers
122 views

How should I define the reward function to solve the Wumpus game with deep Q-learning?

I'm writing a DQN agent for the Wumpus game. Is the reward function to train the Q-networks (target network and policy) the same as the score of the game, i.e. +1000 for picking up gold, -1000 for ...
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1answer
94 views

Reinforcement learning simple problem: agent not learning, wrong action

I am pretty new to RL and I am trying to code a simple RL task with pytorch. The goal/task is the following: The initial state is $t_o$ and the agent takes an action $\Delta_t$: $t_o +\Delta_t = t_1$. ...
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31 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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1answer
81 views

Why do my rewards reduce after extensive training using D3QN?

I am running a drone simulator for collision avoidance using a slight variant of D3QN. The training is usually costly (runs for at least a week) and I have observed that reward function gradually ...
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2answers
96 views

How does one know that a problem is “model-free” in reinforcement learning?

Consider this slide from a Stanford lecture on reinforcement learning. It states that a model is the agent's representation of how the world changes in response to the agent's action. I've been ...
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24 views

Reward and loss follow the same shape in DQN

If the accumulated reward increases, the loss increases and vice versa. This is a strange behaviour. See the figure below for an example. What is the possibility of having this behaviour in DQN? I ...
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23 views

Is it feasible to train a DQN with thousands of input ports?

I designed a DQN architecture for some problem. The problem has a parameter $m$ as the number of clients. In my situation, $m$ is large, $m\in\{100,200,\ldots,1000\}$. For this situation, the number ...
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1answer
49 views

What is the optimal exploration-exploitation trade-off in Q*bert?

I am training an RL agent with Deep Q-learning + Experience Replay on the Q*bert Atari environment. After 400,000 frames, my agent appears to have learned strategic information about the game, but ...
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1answer
38 views

How to calculate v min and v max for C51 DQN

Background: In C51 DQNs you must specify a v-min/max to be used during training. The way this is generally done is you take the max score possible for the game and set that to v-max, then v-min is ...
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1answer
82 views

Is there a logical method of deducing an optimal batch size when training a Deep Q-learning agent with experience replay?

I am training an RL agent using Deep-Q learning with experience replay. At each frame, I am currently sampling 32 random transitions from a queue which stores a maximum of 20000 and training as ...

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