Questions tagged [dqn]

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21 views

Can Online DQN model overfit?

I am new in the area of RL and currently trying to train an online DQN model. Can an online model overfit since its always learning? and how can I tell if that happens?
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31 views

Am I using this neural network in a wrong way?

I'm trying to solve a RL problem; the Contextual Bandit problem using Deep Q Learning. My data is all simulated. I have this environment: ...
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38 views

Cartpole - Number of layers and neurons - model hyperparameters

Can anyone please suggest me how to arrive to the best optimal values for number of layers, number of neurons parameters of the deep learning model in DDQN algorithm for cartpole problem. As input and ...
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20 views

Cannot train DQN to solve cartpole

This is the code of my DQN implementation. I have checked with codes in many other people's repositories. I cannot find any differences but it turns out my codes cannot train the model but theirs can. ...
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62 views

Catastrophic Forgetting on DQN

I'm trying to explore solving the shortest path algorithm using DQN i know we can solve it using the Q-tablebut I just wanted to ...
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29 views

Deep-DQN implementation that works with (state,action) pairs

I'm moderately new to RL(Reinforcement learning) and trying to solve a problem by using a deep Q learning agent (trying a bunch of algorithms) and I don't want to implement my own agent (I would ...
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12 views

In reinforcement learning, continue update weights after training?

Assuming an rl agent that has to be trained, and then you can mainain same weights over episodes: in a single episode, you have to firtly backup weights, udpate continuosly weights in the episodes, ...
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1answer
51 views

In DQN, why not use target network to predict current state Q values?

In DQN, why not use target network to predict current state Q values, and not only next state q values? In doing a basic dq learning algorithm with nn from scratch, with replay memory, and minibatch ...
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49 views

DQN CartPole-v1 neural network doesn't optimize

I'm doing my first dnq algorithm, I'm trying to build a dnq agent, and neural network from scratch, but it seems that neural network doesn't optimize, I did 2 hidden layers, with ReLU, and the output ...
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56 views

Q values loss per episode and mean absolute error

I am new to deep reinforcement learning! I am following this code for my adaptation problem (doing actions) https://github.com/jaromiru/AI-blog/blob/master/CartPole-DQN.py I am wondering how I can ...
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1answer
49 views

Dimensionality of the target for DQN agent training

From what I understand, a DQN agent has as many outputs as there are actions (for each state). If we consider a scalar state with 4 actions, that would mean that the DQN would have a 4 dimensional ...
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13 views

How often to call DQN Replay memory?

I am noob to Machine Learning. I am running a DQN code, I understand the concept of Replay Memory but I don't understand how often shoud it be run. I saw one code calling it once in every 'episode' ...
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25 views

What are the differences between DQN DDQN and Dueling Q network?

What are the differences between DQN DDQN and Dueling Q network? And how they expressed in the agent's behaviour? (For example in an Open AI Gym environment)
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76 views

DQN DDQN and 3DQN differences?

I'm doing a course on reinforcement learning, and one of our tasks is to implement an agent on the Lunar lander continuous V2 environment from openAI gym. In order to solve the continuous problem, I ...
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10 views

Implementing DQN - Pylessons.com article - Queries [closed]

I have followed the below link to implement DQN Algorithm https://pylessons.com/CartPole-reinforcement-learning/ Can someone explain me: Why do we need to have else condition in act function during ...
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2answers
398 views

How exactly does DQN learn?

I created my custom environment in gym, which is a maze. I use a DQN model with ...
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26 views

Epochs and other hyperparameters in Deep Q-Networks

I was wondering about hyperparameters used in Deep Q-Networks. Considering the use of replay memory and target network, together with the epsilon-greedy policy, are the number of epochs different of 1 ...
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1answer
1k views

DQN with decaying epsilon

I'm new to reinforcement learning. I'm studying DQN with decaying epsilon. I came across such example: EPISODES = 91 GAMMA = 0.2 EPSILON_DECAY = 0.999 MIN_EPSILON = 0.01 MAX_EPSILON = 1 My questions ...
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36 views

In a double Deep Q network what would happen if we switch the roles of both networks

We normally use the online network for action selection and the target network for evaluation , would there be a difference if we switched the roles? Because in the case Of Double Q learning, we ...
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98 views

Prioritized Experience Replay - which version is correct?

After reading a lot of stuff, I'm still not sure how to calculate the priorities for Prioritized Experience Replay (PER). Example code taken from here ...
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1answer
167 views

Is this a valid stability concern/improvement for DQN/DDQN reinforcement training?

As you all know, DQN or DDQN are known for "unstable training". Let's use the well known "CartPole". The agent has to balance the stick and gets a reward of +1 per frame. You can reach the 195 ...
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90 views

how do deep Q network deal with varying input size?

I am conducting research with multiply agents in an environment. The main concept of my methodology is a centralized control system, which means we take the positions, as well as other information, of ...
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1answer
39 views

Reinforcement (Q) learning: does it learn while in production?

I have a question for which I could not find the answer to it: While training reinforcement learning (using DQN), I get a model for the best reward for the next action. Now, if I deploy this model (i....
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1answer
83 views

How do I build a DQN which selects the correct objects in an environment based on the environment state?

I have an environment with 4 objects in it. All of these objects can either be selected or not selected. So the actions taken by my DQN should look like - ...
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1answer
88 views

Is it possible to solve Rubik's cube using DQN?

I'm trying to solve Rubik's cube using deep learning and I came across with DQN, so I decided to give it a try. I developed all the code and started training but I got this results: Loss goes up and ...
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2answers
42 views

Free a bit of RAM space

Here is Colab Notebook After 1500 episodes if batch_size=256, the RAM crashed. With Colab, I have the equivalent of 25.5 gigs of RAM. Is it normal? Or I don't have ...
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1answer
78 views

TF2.0 DQN has a loss of 0?

I am having a hard time understanding why my loss is constantly a zero when using DQN. I'm trying to use the gym environment to play the game ...
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1answer
202 views

Representation of state space, action space and reward system for RL porblem

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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0answers
97 views

Q-learning, state transition, immediate rewards (trading logic)

I've been thinking about how to correctly calculate rewards for several weeks now. Here is a grid example: ...
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1answer
365 views

When should the last action be included in the state in reinforcement learning?

I am having some confusion as to whether the action should be included as part of the state input to an agent in a reinforcement learning setting (state-action pair). As from my observation, this is ...
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1answer
428 views

Would Deep Q Learning work for a finite horizon problem?

I want to apply Deep Q Learning to a problem, which has a clear finite horizon definition, like: $$V(s) = \mathbb{E}[r_1 + r_2]$$ Since the horizon is finite, I do not use reward discounting. My ...
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1answer
119 views

Having a reward structure which gives high positive rewards compared to the negative rewards

I am training an RL agent using PPO algorithm for a control problem. The objective of the agent is to maintain temperature in a room. It is an episodic task with episode length of 9 hrs and step size(...
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1answer
66 views

How to handle differences between training and deploying of an RL agent

Hi I am training an RL agent for a control problem. The objective of the agent is to maintain temperature in a zone. It is an episodic task with episode length of 10 hrs and actions being taken every ...
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1answer
474 views

Different results every time I train a reinforcement learning agent

I am training an RL agent for a control problem using PPO algorithm. I am using stable-baselines library for it. The objective of an agent is to maintain a temperature of 24 deg in a zone and it ...
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1answer
3k views

Evaluating a trained Reinforcement Learning Agent?

I am new to reinforcement learning agent training. I have read about PPO algorithm and used stable baselines library to train an agent using PPO. So my question here is how do I evaluate a trained RL ...
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1answer
2k views

Policy Gradient with continuous action space

How to apply reinforce/policy-gradient algorithms for continuous action space. I have learnt that one of the advantages of policy gradients is , it is applicable for continuous action space. One way I ...
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1answer
438 views

Deep reinforcement learning on changing data sizes

I have a game that I want to build a model that will learn to play the game. Yet, the environment output is two lists that represent the location and number of soldiers of the user and Opponent. The ...
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2answers
3k views

Agent always takes a same action in DQN - Reinforcement Learning

I have trained an RL agent using DQN algorithm. After 20000 episodes my rewards are converged. Now when I test this agent, the agent is always taking the same action , irrespective of state. I find ...
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2answers
465 views

How to calculate Temperature variable in softmax(boltzmann) exploration

Hi I am developing a reinforcement learning agent for a continous state/discrete action space. I am trying to use boltmzann/softmax exploration as action selection strategy. My action space is of size ...
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0answers
278 views

Reducing the training time of an RL agent

I am trying to develop an rl agent using DQN algorithm.During training, the agent interacts with environment which is a simulated one.Each episode takes around 10 mins to run. This way if want my ...
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1answer
124 views

How to formulate reward of an rl agent with two objectives

I have started learning reinforcement learning and trying to apply it for my use case. I am developing an rl agent which can maintain temperature at a particular value, and minimize the energy ...
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1answer
61 views

Is DQN limited to working with only image frames?

I have few questions about Deep Q Network. Does DQN only accept image frames as input? I have never hear (read) a paperwork where it doesn't use image frames. If the first is a No, then does image ...
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0answers
62 views

How can I improve the performance of my DQN?

I created a deep Q network to play snake. The code works fine, except for the fact that performance doesn't really improve over the training cycle. At the end, it's pretty much indistinguishable from ...
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1answer
64 views

Deep Q-Learning for physical quantity: q-values distribution not as expected

Setting I am trying to learn a specific physical quantity (radiance) inside a 3D scene with Deep Q-Learning. Just to give a quick overview, my agent shoots rays inside the scene: the reward is the ...
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51 views

DQNs for huge or continuous state spaces [closed]

Have there been occasions where DQN failed to deal with huge state spaces? Can you point out a research paper regarding it?
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1answer
1k views

Difference between Dueling DQN and Double DQN?

I have read some articles, but still can not figure out the difference between the Dueling DQN and Double DQN? What exactly is the difference between them? Also, Does Dueling DQN need to be built on ...
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397 views

How to train DDQN model in reinforcement learning?

I was reading through some RL code but couldn't understand one small bit. here is the code: ...
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1answer
398 views

How does DQN solve Open AI Cartpole - v0?

Context I am confused about how a DQN is supposed to solve the cart pole problem since the rewards are so dense. I have been using pytorch example. I am aware of some solutions, but I have issue with ...
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1answer
1k views

Q-Learning experience replay: how to feed the neural network?

I'm trying to replicate the DQN Atari experiment. Actually my DQN isn't performing well; checking another one's codes, I saw something about experience replay which I don't understand. First, when you ...
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1answer
848 views

DQN fails to find optimal policy

Based on DeepMind publication, I've recreated the environment and I am trying to make the DQN find and converge to an optimal policy. The task of an agent is to learn how to sustainably collect apples ...