Skip to main content

Questions tagged [dqn]

The tag has no usage guidance, but it has a tag wiki.

Filter by
Sorted by
Tagged with
2 votes
1 answer
159 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 ...
4 votes
1 answer
1k 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 ...
0 votes
0 answers
5 views

"Unique" or "Repeated" experiences in memory replay...?

I'm training an RL agent/model (DRL/DQN). Say that, for each learning step, the memory replay used by the agent to learn, has N elements (experiences) stored, where only X are unique elements (...
1 vote
2 answers
767 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 ...
3 votes
1 answer
244 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 ...
0 votes
1 answer
1k views

gym car racing v0 using DQN

I am currently learning reinforcement learning and wanted to use it on the car racing-v0 environment. I have successfully made it using PPO algorithm and now I want to use a DQN algorithm but when I ...
1 vote
1 answer
63 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?
2 votes
1 answer
136 views

How we can have RF-QLearning or SVR-QLearning (Combine these algorithm with a Q-Learning )

How we can have RF-QLearning or SVR-QLearning (Combine these algorithm with a Q-Learning )? I want to replace the DNN section of Qlearning with a RF or SVR but the problem is that there is no clear ...
1 vote
1 answer
193 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 ...
1 vote
0 answers
34 views

Building an engine for a chess-like game

I'm working on building an AI for a chess-like game. I've implemented a Monty Carlo Tree Search (for the early game) and Rainbow DQN (for the mid to late game), and will be implementing Alpha Beta ...
0 votes
2 answers
199 views

In a convolutional layer, is it standard practice to modify stride and padding to get a desired output?

I'm trying to implement the CNN described in A Framework of Hierarchical Deep Q-Network for Portfolio Management (see screenshot). In the paper, the author describes the first CNN layer as having a ...
0 votes
0 answers
26 views

Feed two matrices into an mlp neural network

So Im trying to train a recommender system (w/ DQN) using two sets of data , first is a 2D array size $N\times N$ where the diagonal is the current content (= state) and the rest row is the ...
3 votes
0 answers
725 views

Deep Reinforcement Learning for dynamic pricing

I am trying to implement a Deep Q Network model for Dynamic pricing in Logistics. I can define State Space (Origin, Destination, type of the shipment, customer, Type of the product, Commodity of the ...
0 votes
0 answers
20 views

How to decide a State for Deep Q Learning for Production Line scheduling

There is a production floor with W workstations and N jobs with M operations( different processing times per operation ). A job is completed only if its M Operations are completed. Objective is to ...
7 votes
3 answers
4k views

Why random sample from replay for DQN?

I'm trying to gain an intuitive understanding of deep reinforcement learning. In deep Q-networks (DQN) we store all actions/environments/rewards in a memory array and at the end of the episode, "...
2 votes
1 answer
1k 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 ...
1 vote
0 answers
245 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 ...
2 votes
1 answer
187 views

How to Form the Training Examples for Deep Q Network in Reinforcement Learning?

Trying to pick up basics of reinforcement learning by self-study from some blogs and texts. Forgive me if the question is too basic and different bits that I understand are a bit messy, but even after ...
2 votes
1 answer
6k 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 ...
1 vote
0 answers
1k views

DQN cannot learn or converge

I have implemented a DQN using keras. The task is to collect the circles and avoid the red circle and crosses. The associated rewards are +5, -5 and 0 otherwise. if the agent go out of the board, the ...
1 vote
1 answer
186 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 ...
1 vote
0 answers
208 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 ...
0 votes
0 answers
90 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 ...
3 votes
1 answer
5k 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 ...
1 vote
0 answers
271 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 ...
1 vote
0 answers
11 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 ...
3 votes
2 answers
513 views

How exactly does DQN learn?

I created my custom environment in gym, which is a maze. I use a DQN model with ...
1 vote
1 answer
130 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 ...
1 vote
0 answers
32 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 ...
2 votes
1 answer
5k 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 ...
1 vote
0 answers
37 views

What is the meaning of the Variant Q-learning and To what INPUT and OUTPUT refer? in Abstract of DeepMind DQN paper 2013

-INPUT and OUTPUT OF ATARI DQN: In the abstract paragraph of the DQN work by DeepMind it has written: " We present the first deep learning model to successfully learn control policies directly ...
1 vote
0 answers
49 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 ...
1 vote
0 answers
151 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 ...
0 votes
1 answer
316 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 ...
0 votes
1 answer
70 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....
2 votes
1 answer
193 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 - ...
1 vote
0 answers
121 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: ...
6 votes
2 answers
2k views

How to choose between discounted reward and average reward?

How to select between average reward and discounted reward? And when average reward is more effective in comparison with discounter reward and when vice versa is correct? Is is possible to use both ...
1 vote
2 answers
87 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 ...
11 votes
2 answers
3k views

How does Implicit Quantile-Regression Network (IQN) differ from QR-DQN?

For several months I browsed the internet hoping to find a user-friendly explanation of the Implicit Quantile Regression Network (IQN). But, it seems there is none at all. How does IQN differ from ...
2 votes
1 answer
363 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 ...
7 votes
1 answer
4k views

What are the effects of clipping the reward in stability?

I am looking for stabilizing my results of DQN, I found clipping is one technique to do it but I did not understand it completely! 1- what are the effects of clipping the reward, clipping the ...
4 votes
2 answers
5k 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 ...
4 votes
1 answer
2k views

What is a minimal setup to solve the CartPole-v0 with DQN?

I solved the CartPole-v0 with a CEM agent pretty easily (experiments and code), but I struggle to find a setup which works with DQN. Do you know which parameters should be adjusted so that the mean ...
2 votes
1 answer
825 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 ...
10 votes
2 answers
18k views

what is difference between the DDQN and DQN?

I think I did not understand what is the difference between DQN and DDQN in implementation. I understand that we change the traget network during the running of DDQN but I do not understand how it is ...
2 votes
1 answer
572 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(...
0 votes
1 answer
225 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 ...
1 vote
1 answer
1k 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 ...
3 votes
1 answer
963 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 ...