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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Atari Breakout Infrastructure

This is how they describe their infrastructure in https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf. I want to implement the game of Atari Breakout. ...
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24 views

Whats the correct loss function to use during deep Q-learning (discrete action space)

After playing around with normal Q-learning I have decided to switch to deep Q-learning and I have encountered this problem. As I understand, for a task with discrete action space, where there are 4 ...
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Why is my DQN model not getting better?

I've created a deep Q network. My model does not get better, and can't see what I'm doing wrong. I'm new to RL. Replay Memory ...
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2answers
212 views

What are some online courses for deep reinforcement learning?

What are some (good) online courses for deep reinforcement learning? I would like the course to be both programming and theoretical. I really liked David Silver's course, but the course dates from ...
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43 views

Representation of state space, action space and reward system for Reinforcement Learning problem

I am trying to solve the problem of an agent dynamically discovering(start with no information about the environment) the environment and to explore as much of the environment as possible without ...
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91 views

How to correctly implement self-play with DQN?

I have an environment where an agent faces an equal opponent, and while I've achieved OK performance implementing DQN and treating the opponent as a part of the environment, I think performance would ...
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2answers
138 views

Why are reinforcement learning methods sample inefficient?

Reinforcement learning methods are considered to be extremely sample inefficient. For example, in a recent DeepMind paper by Hessel et al., they showed that in order to reach human-level performance ...
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1answer
54 views

How does the optimization process in hindsight experience replay exactly work?

I was reading the following research paper Hindsight Experience Replay. This is the paper that introduces a concept called Hindsight Experience Replay (HER), which basically attempts to alleviate the ...
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1answer
53 views

Can experience replay be used for training after completing every single epoch?

The DQN implements replay memory. Based on my research, I believe the replay memory starts to get used for training once there is enough experience in the memory buffer. This means the neural network ...
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1answer
44 views

Intutitive explanation of why Experience Replay is used in a Deep Q Network?

I understand that Experience Replay is used for data efficiency reasons and to remove correlations in sequences of data. How exactly do these sequences of correlated data affect the performance of the ...
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1answer
97 views

How is the expected value in the loss function of DQN approximated?

In Deep Q Learning the parametrized Q-functions $Q_i$ are optimised by performing gradient descent on the series of loss functions $L_i(\theta_i)= E_{(s,a)\sim p}[(y_i-Q(s,a;\theta_i))^2]$ , where ...
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127 views

What's the difference in using multiple convolutional layers and no pooling versus using a single convolutional layer and a single max pooling layer?

I'm currently working on a college project in which I'm designing a Deep Q-Network that takes images/frames as an input. I've been searching online to see how other people have designed their ...
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1answer
122 views

Why isn't my implementation of DQN using TensorFlow on the FrozenWorld environment working?

I am trying to test DQN on FrozenWorld environment in gym using TensorFlow 2.x. The update rule is (off policy) $$Q(s,a) \leftarrow Q(s,a)+\alpha (r+\gamma~ max_{a'}Q(s',a')-Q(s,a))$$ I am using an ...
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1answer
103 views

Why does reinforcement learning using a non-linear function approximator diverge when using strongly correlated data as input?

While reading the DQN paper, I found that randomly selecting and learning samples reduced divergence in RL using a non-linear function approximator (e.g a neural network). So why does Reinforcement ...
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2answers
424 views

Understanding the loss function in deep Q-learning

I am trying to understand how deep Q learning (DQN) works. To my current understanding, each $Q(s, a)$ functions is estimated to be a function of a feature vector of its state $\phi$(s) and the weight ...
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1answer
106 views

How can a DQN backpropagate its loss?

I'm currently trying to take the next step in deep learning. I managed so far to write my own basic feed-forward network in python without any frameworks (just numpy and pandas), so I think I ...
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1answer
75 views

NoisyNet DQN with default parameters not exploring

I implemented a DQN algorithm that plays OpenAIs Cartpole environment. The NN architecture consists of 3 normal linear layers that encode the state, and one noisy linear layer, that predicts the Q ...
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36 views

Is the training of multi-version of the same system at the same time affecting the results?

I'm using DQN to train multi-version of the same system and there is a small difference when I run them both separately. However, my result suddenly dropped in both versions if I run them both at the ...
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DQN, how to choose the reward fucntion?

I built a simple AI system that tries to solve the 8 puzzle using DQN. The problem is, if the agent gets only a reward greater than zero when winning, the training will take a long time, so I made a ...
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1answer
113 views

DQN card game - how to represent the actions?

I want to train a DQN card game named witches. It consists of 60 Cards (1-14 of Yellow, Blue, Green, Red Cards + 4 Wizzards). The color of the first layed card has to be respected by the other players ...
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28 views

What could be the cause of the drop of the total reward when using DQN to solve the cart-pole environment?

I'm trying to use DQN to solve the cart-pole environment. I have 2 networks (target and behavior). Both of them have 3 hidden layers with 24 neurons, using the ReLU activation. The loss is MSE and the ...
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17 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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47 views

DQN unlearns certain OpenAI-Gym environments

I solved the OpenAI-Gym MountainCar-v0 environment using dqn(using low-state-dimensional input). When I used the same code for solving CartPole-v0 environment, the network got trained in the reverse ...
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43 views

What is the difference between random and sequential sampling from the reply memory?

I was working on an RL problem and I am confused at one specific point. We use replay memory so that the network learns about previous actions and how these actions lead to a success or a failure. ...
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1answer
53 views

DQN regarding fitting off actions

While studying DQN, I rarely if ever see an explanation of what to fit off actions to. Meaning, if the highest valued action of 3 choices if the first, when training, what do we do with the other two ...
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1answer
43 views

How important is the choice of the initial state?

Is it crucial to always have the same initial (starting) state for Reinforcement Learning, for example, for Q-learning or DQN? Or it can vary?
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1answer
521 views

How is the gradient of the loss function in DQN derived?

In the original DQN paper, page 1, the loss function of the DQN is $$ L_{i}(\theta_{i}) = \mathbb{E}_{(s,a,r,s') \sim U(D)} [(r+\gamma \max_{a'} Q(s',a',\theta_{i}^{-}) - Q(s,a;\theta_{i}))^2] $$ ...
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1answer
42 views

Is a state that includes only the past n-step price records partially observable?

I'm currently working on a project to make an DQN agent that decides whether to charge or discharge an electric vehicle according to hourly changing price to sell or buy. The price pattern also varies ...
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29 views

Should importance sample weighting be compensated for by dynamically increasing learning rate?

I'm using Prioritized Experience Replay (PER) with a DDQN. To compensate for overfitting relatively high-value samples due to the non-uniform selection, I'm training with sample weights provided along ...
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1answer
82 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
239 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 ...
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1answer
85 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
54 views

How to represent players in a multi agent environment so each model can distinguish its own player

So I have 2 models trained with the DQN algorithm that I want to train in a multi-agent environment to see how they react with each other. The models were trained in an environment consisting of 0's ...
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1answer
45 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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1answer
158 views

Is DDPG just for deterministic environments?

I want to develop an AI for continuous space. I reached to DDPG algorithm that takes actions deterministically. If DDPG takes actions deterministically, should the environment also be deterministic? ...
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1answer
287 views

Why does the DQN not converge when the start or goal states can change dynamically?

I'm trying to apply a DQN to a stochastic environment, but I'm having trouble getting it to converge. I found some similar questions asked here, but no solutions yet. I can easily get the DQN to ...
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1answer
399 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
243 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 ...
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155 views

Deep Q-Network (DQN) to learn the game 2048

I am trying to build a Deep Q-Network (DQN) agent that can learn to play the game 2048. I am orientating myself on other programs and articles that are based on the game snake and it worked well (...
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196 views

Gym dict space as keras DQN agent input

I'm trying to make an AI to play my own card game. I have an OpenAI gym for the game with Dict as an observation space. It is nested dict, so I can't easily replace it with a tuple. I want to pass ...
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100 views

Online normalization of database for DQN

I have an issue with the normalization of the database (a large time series) for my DQN. I obtained optimal results and saved the NN (5 LSTM layers) weights training on a database normalized as such: ...
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1answer
75 views

New transition priorities in Prioritized Experience Replay?

I am having a hard time converting line 6 of the prioritized experience replay algorithm from the original paper into plain English (see below): I understand that new transitions (not visited before) ...
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320 views

What could be causing the drastic performance drop of the DQN model on the Pong environment?

I am running a basic DQN (Deep Q-Network) on the Pong environment. Not a CNN, just a 3 layer linear neural net with ReLUs. It seems to work for the most part, but at some point, my model suffers from ...
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1answer
78 views

Why do authors track $\gamma_t$ in Prioritized Experience Replay Paper?

In the original prioritized experience replay paper, the authors track $\gamma_t$ in every state transition tuple (see line 6 in algorithm below): Why do the authors track this at every time step? ...
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1answer
44 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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21 views

DQN ANN input vs Linear function approximator feature vector

So when using semi-gradient td(0) you need to convert your state representation into a feature vector that represents the state and as far as I know, should not be correlated. Is the input on the ANN ...
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1answer
366 views

Which kind of prioritized experience replay should I use?

The Prioritized Experience Replay paper gives two different ways of sampling from the replay buffer. One, called "proportional prioritization", assigns each transition a priority proportional to its ...
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1answer
94 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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4answers
300 views

How to stop DQN Q function from increasing during learning?

Following the DQN algorithm with experience replay: We calculate the $loss=(Q(s,a)-(r+Q(s+1,a)))^2$. Assume I have positive but changing rewards. Meaning, $r>0$. Thus, since the rewards are ...
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90 views

Why experience reply memory in DQN instead of a RNN memory?

I was trying to implement a DQN without experience reply memory, and the agent is not learning anything at all. I know from readings that experience reply is used for stabilizing gradients. But how ...