Dqn työt

Tensorflow expert to advice on simple tuning a DQN in "already running" code. I just want to do simple things like increasing the number of hidden layers, alter learning rate etc. Your alterations don't have to perform better than the original code at this stage (we'll get to that in future), it just needs to help explore something different.

I am looking for a Machine Learning/DQN expert with experience in Python to implement a simple ridesharing code. The project has a duration of 3 days. Preferred Skills and Experience: - Expertise in Python programming language - Strong knowledge of Machine Learning and Deep Q-Networks (DQN) - Experience in implementing advanced ridesharing algorithms Project Requirements: 1. A lot of the work has been done in this Github repo: 2. Implement the repo ensuring multiple passengers can use a single vehicle when it’s efficient to do so. Make changes only when very necessary, but focus on delivering a clear, usable output. 3. The project is about ridesharing (multiple passengers using single vehicle when feasible), not about

This first phase is really simple and there's a GitHub code repo (see below) that can be built upon. Simply train a model (using DQN) that will pick two people from an origin and decide whether they can share based on their desired arrival times (e.g. if both are going to same destination and have same arrival time, then they can share. If they're going to separate destination and one is going in the morning and the other in the evening, then they have to be taken separately. This GitHub code repo can be modified for just one car and two passengers (assume passengers have same origin address):

Build DRL algorithm eg Policy Gradient or DQN. Using Pytorch. Build environment for scalability. I provide specifications. Two parts. Build 'environment' which is object class following template of gym. Environment with action space, statespace etc. Second part is the algorithm which runs the environment over and over determine optimal policy. An example pf such algo is Deep Q-learning The Deep Q learning algorithm entails a neural network which is why pytorch is needed. Back to the environment: it model cashflows of an insurance policy and the underlying allocation of the premium to assets.

I have created an environment and tried to train a DQN model on it but keep getting error so fix the code ASAP

I coded my environment myself (which is a multipath network) and just need someone to use this environment as the environment of A3C and DQN reinforcement algorithms. I will provide you with the code of the environment.

This Task is to implement Deep Q-Learning Networks (DQN) for an RL agent that lean to play Atari games, in particular the game of Breakout. Your Task: Your task is to implement (at least) ONE DQN agent, report the performances with visualizations, and discuss the results in depth. Requirements: The agent should utilize a Deep Q-learning network as the model. The agent must use TWO networks -- a prediction and a target networks. As a hint, you can call get_weights() and set_weights() in the networks to transfer weights. Also it should use a reply/experience memory. Train the agent/model using various epsilon values. Try different model/training hyperparameters and tune them, as usually done in Deep Learning. A. DQN Agents The agent must be a Convolutional Neural Network...

...values for each combination of state and action. Because of that, in this part, you have to train a Deep Q-Network to estimate the Q-values. Submission: Submit your training and testing code Submit a video that shows cubes are falling to the target after you run your code Submit a .txt file that contain the log of the training, including the accumulated rewards for each episode, TD errors for each DQN update Submit a .txt file with the following result: How many times all the cubes fell successfully to the target in 100 trials if your state space consists only cup position? How many times all the cubes fell successfully to the target in 100 trials if your state space consists both cup position and two cube’s position? ...

An RL based approach to schedule the URLLC users by preempting the ongoing eMBB user traffic. The approach targets to get low latency and high reliability for urllc users. Preemption is done such that fairness is maintained among the eMBB users.

Create a RL algorithm using DQN

Hi all, Here are some screening questions. 1) How would you find a minimum of y=e^(2 - x) + x^2 - x/2? 2) Transformers: can you have 5 attention heads with a sequence length equal to 100 and embedding dimension to 512, and why? 3) Please explain why the Bellman Equation is important and explain how you have used it in your work. 4) DQN RL: how can you use DQN on an environment with 100 unique states, and actions taking values on the interval [-0.5, 0.5]? We would like to provide more details to the right candidate who can give me answers to the 4 questions above. Thank you in advance

I want to build trading bot that swing trades based on machine learning and that sends live buy and sell alerts based on this repo:

I need someone to develop a deep Q learning Network on python for plants versus zombies.

Need an UE4.26 C++ plugin implementation for deep reinforcement learning. Need to have ability to modify a NN model in DQN (modify number of hidden layers and number of neurons in them, modify actualy a model type) Will be a plus if you use such libs as openNN or caffe, because with using python and TF (or similar) it will be a delay between python and c++ comunication. FYI (does NOT need to implement): Agent will be a drone with parameters: position (XYZ), acceleration(XYZ) and destination point (XYZ). Need to learn drone to get to the destination point.

Need an expert in Reinforcement Learning using Double DQN and Dueling DQN

I need some help with my homework. Its a reinforcement learning problem with DQN. I have the solution but in tensorflow. I need to write it in pytorch but i know nothing about pytorch. Can you help me out?

I need some help with my homework that is due tomorrow. Its a reinforcement learning problem DQN and it written in pytorch but i dont have any experience with pytorch. Can you help me out with the code and provide some tutoring?

I have a DQN homework problem that i need help on. I need a tutor. It due tomorrow night so the timing is tight. Can you help me out with some programming and understanding?

Train a DQN and DDQN for atari boxing and compare results.

I am looking for someone who is good at Reinforcement Learning. In our task what we need to do is just implement the DQN or DDQN or any other algorithm on Crafter's Custom Environment that i will provide you !!! That's all you need to do. if you think you can do it !!! Please bid and start working on it immediately. I will provide complete details privately. Thanks

I am looking for someone who is good at Reinforcement Learning. In our task what we need to do is just implement the DQN or DDQN or any other algorithm on Crafter's Custom Environment that i will provide you !!! That's all you need to do. if you think you can do it !!! Please bid and start working on it immediately. I will provide complete details privately. Thanks

I am looking for someone who is good at Reinforcement Learning. In our task what we need to do is just implement the DQN or DDQN or any other algorithm on Crafter's Custom Environment that i will provide you !!! That's all you need to do. if you think you can do it !!! Please bid and start working on it immediately. I will provide complete details privately. Thanks

I am looking for someone who is good at Reinforcement Learning. In our task what we need to do is just implement the DQN or DDQN or any other algorithm on Crafter's Custom Environment that i will provide you !!! That's all you need to do. if you think you can do it !!! Please bid and start working on it immediately. I will provide complete details privately. Thanks

I want to implement a DQN deep reinforcement learning model on a labelled dataset. I will share the related research work and the dataset.

I have a model js based web application and a flask server running a DQN algorithm. I need to integrate the DQN algorithm with the model web site. The model website through a POST request sends JSON data to the flask server where the JSON data needs to be processed as inputs to the DQN algorithm and as a response to the POST request , the output of the algorithm should be passed into the front end web site. Once the outcome from the server is sent to the front end of the website based on that you will need to rearrange the ui elements.

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Respected Members! Hoping for your good and sound health. i have integrated Convolutional Neural network with Feed forward network. My code works fine for Feed forward network, but when i place with CNN it gives e the following error. I have been searched lot of sites and other details but co...works fine for Feed forward network, but when i place with CNN it gives e the following error. I have been searched lot of sites and other details but couldn't work. How to solve this error plz help me. i am using python with spyder IDE InvalidArgumentError: You must feed a value for placeholder tensor 'dqn/InputData/X' with dtype float and shape [?,11,4] [[node dqn/InputData/X (defined at C:UsersHPAnaconda3libsite-packagestflearnlayers) ]] Original stack trace ...

Hello. I have my python DQN-pytorch code but I think I have some of problem in my code. It is working but I don't know where the problem came from. Anyone who is good at python with reinforcement learning, please message me:) We can talk about details. However, I only have a few hours so please message me who can start right away! Thanks.

Hi. I'd like to solve pacman environment using DQN /Double DQN/Multistep agent. There are many layout but smallGrid is our goal. All the environment codes are ready, you only need to add some function of code (a few code of step function) with pytorch. I have a test code so you can test with your code. If you are interested in this project, please message me for the details :) (Btw, I've posted this project before but a freelancer ran away so I'm posting this project again.) Thank you.

Hi. I'd like to solve Open AI's 'taxi-v2' or 'taxi-v3' environment using MONTE CARLO CONTROL agent. I need Q-Learning and MC Control and I've solved Q-Learning but couldn't solve MC Control. Just make sure that I only need MC Control (not MC Predict/TD/DQN/SALSA and so on) I have a test code so you can test with your code. The environment is ready so all I need is to code value/Q function. If you are interested in this project, please message me:) Thank you.

A tutor who is able to teach and train on RIL, QL, and DQN using python is required. The tutoring is completely practical (No theory is required)

An Arabic speaking tutor who is able to teach and train on RIL, QN, and DQN using python. The tutoring is completely practical (No theory is required)

...interface in the browser to connect to the Tensorflow app in Node.js and get the Balance at the end of trading each epoch. So we may see the progress and how it is learning. You will be required to know Reinforcement Learning and have example/s of your work in this area on GitHub so we can verify your previous work. This project will be considered completed once we have the ability to build DQN algorithms on the fly with different configurations of architecture and our trading calculator so we can run multiple sessions in parallel and see the best performing one. A report and the model will need saved at the end of each epoch and be able load a saved model to continue training if interrupted or needs additional training. You will be working alongside another Node.js develop...

Hi I am working in Deep reinforcement learning (DQN) and Double Deep reinforcement learning and I would like to in inject noise to gradient discrete (policy) env = CartPole-v0 . I can seend the coud so you can have look

i am working on deep reinforcement learning. i am stuck with continuous state space value how to handle continuous state value by using quantization for energy efficiency and spectral efficiency. I have created my code initially for power saving by using three Q-learning (dqn, double and dueling) algorithm. Now i have already optimized power and throughput. But i am confused where and how to solve state space by using the concept of quantization or dicretization method. Anyone having handy experience on such kind of problem, i will pay a handsome amount and will handed my python existing code which i did for power saving. I will contribute from myself also. kindly only expert of deep reinforcement learning contact me note: My Aim is to use some formula for energy efficiency and sp...

hello, I need someone help regarding Deep Reinforcement Learning, i am stuck with testing my model. I want to optimize power at the end-user by using DQN double and Dueling. Now after training my model well and put model id for the testing case. its working fine but why i am not getting the last episode training value at the start of testing episodes. and 2ndly the final and initial state is also not changing. kindly help me. i have very short period of time plz help

Hi, I have a detailed system model, with state space, space, what parameter(s) will contribute to the reward, how the system parameters will be evolving with certain input then producing certain output, etc.   Now, I want to apply deep Q-network/reinforcement learning/ some advanced DQN in my model with tensorflow/pytorch.

...learns a sine wave. Currently the DQN learns the game snake. Instead, we want the "game" to plot a sine wave and the agent learns to predict if the next step is 'up' or 'down'. Extras points if if can predict how much up or down. Output - Converted game that uses a sine wave as training input and 'up/down' prediction. - Simple Tensorflowjs network that can track a sine wave. - Must be trainable and runnable. Both the training step and inferences stages must work. - Other signals can be added to the network. (Must be easy to make other signals - 'i.e. saw wave'). - Output should be a plot showing sine wave, prediction and "action" taken by the dqn. - Output should show current network input. Happy to answer an...

Hello, I am seeking a developer who can tur...learns a sine wave. Currently the DQN learns the game snake. Instead, we want the "game" to plot a sine wave and the agent learns to predict if the next step is 'up' or 'down'. Extras points if if can predict how much up or down. Output - Converted game that uses a sine wave as training input and 'up/down' prediction. - Simple Tensorflowjs network that can track a sine wave. - Must be trainable and runnable. Both the training step and inferences stages must work. - Other signals can be added to the network. (Must be easy to make other signals - 'i.e. saw wave'). - Output should be a plot showing sine wave, prediction and "action" taken by the dqn. Happy to answer an...

Hello, I am seeking a developer who can tur...learns a sine wave. Currently the DQN learns the game snake. Instead, we want the "game" to plot a sine wave and the agent learns to predict if the next step is 'up' or 'down'. Extras points if if can predict how much up or down. Output - Converted game that uses a sine wave as training input and 'up/down' prediction. - Simple Tensorflowjs network that can track a sine wave. - Must be trainable and runnable. Both the training step and inferences stages must work. - Other signals can be added to the network. (Must be easy to make other signals - 'i.e. saw wave'). - Output should be a plot showing sine wave, prediction and "action" taken by the dqn. Happy to answer an...

I am here to post about the machine learning project by using TensorFlow Spyder IDE. Any expert in this field to know the optimization process for spectral and energy efficiency by using Dueling Deep Q-network. I will provide the code which I created for power optimization by using DQN. Now I need to define the environment for these two parameters (energy efficiency and spectral efficiency) with proper constraint. for spectral and energy efficiency I will provide MATLAB code too but in this code i use the conventional method where RA is based on the instantaneous decision. I will provide him my existing proper code with a reference paper. Keep in mind a good package will be offered after the completion of my project. kindly read the post clearly before posting your comment. I will n...

I am here to post about the machine learning project by using TensorFlow Spyder IDE. Any expert in this field to know the optimization process for spectral and energy efficiency. I will provide the code which i created for power optimization, now need to define the environment for these two parameters (energy efficiency and spectral efficiency) with proper constraint. i will provide him my existing code with the proper code. A good package will be offered after the completion of my project. kindly read the post clearly before posting your comment

hello, i have a reinforcement learning task where, i need to apply n- step DQN to solve cartpole problem. i already have a code for double DQN in tensorflow. i need to update this code to apply n-step dqn. if you agree then i will provide .ipynb file, where double dqn is implemented. i need it untill friday. thanks.

Hello, i am hiring a AI & Machine Learning Expert which have already developped and programmed many RL programs. Also experience in Gym custom environnement programming. i can explain and share the modelling phase (actions, states etc...) but the code part should be ensured by the hired developer. The project is considered as done after the programmed agent run on my PC and find a good convergence, also the code should be commented in details.

Hello! I need someone who has great experience with RL methods ideally combined with expertise in robotics to design RL (probably DQN) NN and train custom humanoid robot to learn basic tasks like balancing & walking. Not necessary, but for many extra points is if you can consult some of followings: 1, What other tasks could be potentially trained 2, how NN could be used in real HW robot 3, how we can combine your NN with standard robot control approaches.

it's snake game that's able to learn where the bad food and good food using python, algorithm and DQN

Goal: Use the agent to explore the Android UI to reach as many new UI states as possible. By exploring the states, agent hopes to find possible combinations of events/inputs that lead to unintended behavior like app crashes. I have integration Droidbot () with two agents: Actor Critic and DQN. Since I made it a gym environment, you can use any type of algorithm from any RL package that works with gym. I modified Droidbot to work as a gym environment. This allows you to integrate Droidbot with any major Reinforcement Learning library that works with gym environments (which is all of the major ones). I then intergrated the Droidbot gym environment with the Stable Baselines Reinforcement Learning library. I trained multiple agents and provided a script for

1. I want to implement the attached paper typically by two different data set the first one using Geant topology (mentioned on the paper) the other one using a Abilene data set . data set will be sent later. 2. ineed to implement the paper with LSTM , Q learning and deep Q network (DQN) 3. The implementation by using python ,Keras liberary on tensor flow. 4. results and output figures for comparing different prediction methods, as in the attached paper. provided with the complete code . 5. complete explanation for number of hidden layer and training over time .

I have simple project based on Q-Learning. It has to be applied to simple game. Anyone who is willing to apply to this job should check my attached files in advance is my snake game to apply DQN. And other folder contains implementation DQN for so simple snake game. I would like to apply that to my Who can help me? Thanks.

Hello, I found one Github project and trying to try it: This code is one Neural Network and working great in Training mode, but when running "Trading"(test mode), it doesn't work. I wonder if anybody can fix Testing(Trading) mode. Commands for Trading are: python3 train/ -o python3 train/ -t If you fixed it, before I award you, please send some screenshots from results. Regards,