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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124 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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35 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. ...
3
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95 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 (...
3
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51 views

DQN Agent not learning anymore - what can I do to fix this?

I am trying to use Deep-Q-Learning to learn an ANN which controls a 7-DOF robotic arm. The robotic arm must avoid an obstacle and reach a target. I have implemented a number of state-of-art ...
3
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0answers
110 views

What can be considered a deep recurrent neural network?

In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the DRQN is described as DQN with the first post-convolutional fully-connected layer replaced by a recurrent LSTM. I have DQN ...
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30 views

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 ...
2
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23 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 ...
2
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27 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 ...
2
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1answer
32 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 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 and 1'...
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39 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 ...
2
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1answer
112 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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21 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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22 views

Why isn't this deep Q network agent for the snake game learning?

I wanted to combine this snake game with this DQN implementation I found in this article. First, I tried to change the NN's input layer to a 400 input. The game has a field of 20 times 20, so I ...
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13 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 ...
1
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1answer
91 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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0answers
83 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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0answers
16 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 ...
1
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0answers
23 views

Deciding the rewards for different actions in Pong for a DQN agent

I am attempting to implement an agent that learns to play in the Pong environment, the environment was created in PyGame and I return the pixel data and score at each frame. I use a CNN to take a ...
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53 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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32 views

Comparison and understanding of different version of DDQN?

There are several version of DDQN floating around. Sutton gives one that is a simple symmetric random update of the two Q functions. I think other papers (Silver paper for example) use a kind of ...
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39 views

Exploration rate decay and training in Q learning

I'm trying to replicate the results of the DeepMind's paper with Breakout included in OpenAI Gym. I wonder how much frames should I keep until I reach the fixed exploration rate. Actually it reaches ...
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52 views

Deep Q-Network concepts and implementation

How does sequential DQN work? How would one construct the simple sequential DQN? OpenAI Baselines: DQN
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27 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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19 views

Suggestion on How to Push SARS to Memory Buffer of Losing RL Agent in Adversarial Learning

I am trying to implement my first RL program where there are multiple agents, rather than just one. The environment I am using is the connect four game, which is turn-based. In DQ-Learning, an agent ...
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53 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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55 views

DQN Algorithm not Converging for HVAC Control Task

I am trying to implement the DQN algorithm for the task of HVAC control. I have the algorithm implemented using Pytorch. I know that the HVAC simulator is working as it works for other control methods....
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26 views

Next step after lane detection in vehicle automation

I have an RC car with a camera, I have implemented so that i can detect lanes on my track (think like a nascar track). I want to get this car to be able to go around the track autonomous. But I am ...
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144 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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62 views

Having trouble solving Pong. My model is not improving

Im trying to solve pong by a DQN approach. These are my hyper parameters: ...
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1answer
139 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 ...