All classes 5G Deep RL Resource Allocation Syllabus · Episode 5 of 6

Class 5G Resource Allocation Deep RL #5: Tutorial Deep Q-Network from Scratch with PyTorch for Resource Block and 5G Power Allocation: replay buffer, target network, Double DQN. Episode 5 of the 5G Resource Allocation Deep Reinforcement Learning Series.

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Tutorial Alokasi Sumber Daya 5G Deep RL #5: Training a Deep Q-Network Agent for Resource Allocation

Software: Python (NumPy, PyTorch, Gymnasium, Matplotlib)
Level: Advanced
Duration: ± 11 min
Series total: 1 h 1 min · 6 videos
Published: October 25, 2026

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Class 5G Resource Allocation Deep RL #5: Tutorial Deep Q-Network from Scratch with PyTorch for Resource Block and 5G Power Allocation: replay buffer, target network, Double DQN. Episode 5 of the 5G Resource Allocation Deep Reinforcement Learning Series.: Cover: Class 5G Resource Allocation with Deep RL #5: Training a Deep Q-Network Agent for Resource Allocation

Cover: Class 5G Resource Allocation with Deep RL #5: Training a Deep Q-Network Agent for Resource Allocation

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Introduction

This is the final result, the DeepQ Network agent we wrote from scratch with PyTorch, learning to schedule resource blocks and manage power solely through trial and error. In just a few minutes, the throughput increases from around 8 to 20 megabits per second. Let's get started. Hello, welcome to episode five of the 5G resource allocation series with Deep Reinforcement Learning. The environment is now ready. Today we are writing the DeepQ Network algorithm, an algorithm famous for successfully playing Atari games at a human level, and training it to allocate network resources.

Step 1: O & Replay Buffer Network

Create file dekien.pe The Q network is a multilayer perceptron with two hidden layers of 128 neurons, which receives the state and outputs Q values for the 24 actions, which is an estimate of the total future reward if that action is taken. The Ripley buffer stores up to 100,000 transitions, then takes random samples for training, so that sequential and correlated data does not disrupt learning.

Step 2: DQN & Double DQN Agent

The dekien agent class has two networks. The trained Q network, and the target network which is copied every 1,000 steps to keep the target learning stable. The select function uses epsilon greedy, with the epsilon probability agent trying a random action for exploration, otherwise selecting the action with the highest Q value. The learn function uses double the QN target, the Q network selects the best action in the next state, the target network evaluates it, so the estimated value isn't too optimistic. The Uber loss and gradient clipping make training resilient to large rewards.

Step 3: Training the Agent

Create a train file named dekien.py We train for 30 episodes, each with 200 slots or 2000 decisions, with a different random network in each episode so the agent doesn't memorize one layout. Epsilon decreases linearly from 1 to 0.05 in the first part of training, and the agent learns every two decisions. After completion, the weights are saved to dekien_ allocation.pt, then we plot the training curve.

Step 4: Run Training & Curve

Run At the beginning, when the agent is still trying many random actions, the throughput is around 8 to 15 megabits per second. After epsilon is small, the reward rises above 1700 and throughput is around 20 megabits per second, with energy efficiency above 2 megabits per joule. Let's look at the graph. Total reward and throughput spike after episode 15, when the agent stops exploring and starts using its knowledge. The ups and downs are normal because each episode uses an old layout, with different users. Some are easy and some are hard. Loss hue increases at the beginning when the buffer starts filling with various experiences, then slowly decreases as the estimated queue value becomes more accurate. Done. We have written a deep queue network from scratch with ripley buffer,

» Done!

epsilon greedy, target network, and double the QN, then trained the resource allocation agent. In the final episode, we evaluated it against all benchmark schedulers. Working on a thesis, dissertation, or research in the field of telecommunications and artificial intelligence? Cerdas Inovasi Digital helps implement cellular network research, from 5G simulators, channel models and SINR, proportional fair and waterfilling schedulers, up to deep reinforcement learning with PyTorch, as well as offering Python programming courses for electrical engineering and telecommunications. Want to master the entire series, from resource block concepts to QN agent energy efficiency evaluation? All 6 episodes, complete with PDF step-by-step guides and source code, are available to premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:42Step 1: O & Replay Buffer Network

3:11Step 2: DQN & Double DQN Agent

5:57Step 3: Training the Agent

9:01Step 4: Run Training & Curve

9:52» Done!

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Transcript

0:03Introduction

0:42Step 1: O & Replay Buffer Network

3:11Step 2: DQN & Double DQN Agent

5:57Step 3: Training the Agent

9:01Step 4: Run Training & Curve

9:52» Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: O & Replay Buffer Network

Step 2: DQN & Double DQN Agent

Step 3: Training the Agent

Step 4: Run Training & Curve

» Done!

Introduction

Step 1: O & Replay Buffer Network

Step 2: DQN & Double DQN Agent

Step 3: Training the Agent

Step 4: Run Training & Curve

» Done!