Training

Overview of the RL Agent and Training Procedure

In this section, we will focus on the core mechanism behind the Reinforcement Learning (RL) agent, how it optimizes the circuit parameters, and the details of the PPO (Proximal Policy Optimization) algorithm.

4.1 RL Agent Overview

The RL agent is the core component responsible for learning how to modify the circuit parameters, such as transistor sizes and compensation capacitor values, to achieve the desired performance. It operates through interactions with the simulation environment, making decisions based on the current state of the circuit and receiving feedback in the form of rewards.

Key Components of the RL Agent:

• State Space: The current configuration of the circuit (e.g., transistor dimensions and capacitor values).
• Action Space: The set of all potential adjustments to circuit parameters.
• Reward Function: A scalar value representing the performance of the circuit, where a higher reward indicates better performance (e.g., higher gain or bandwidth).
• Policy: The strategy used by the agent to decide which action to take, which is optimized during training.

The agent uses the PPO algorithm to adjust its policy over time based on feedback received from the simulation.

4.2 PPO Algorithm: Policy Optimization

The PPO algorithm is one of the most effective methods for training RL agents with continuous action spaces. It performs policy optimization by iteratively adjusting the agent’s policy based on feedback. The PPO method includes a key feature: it ensures that the policy update stays within a safe region, preventing large, destabilizing updates.

PPO Key Concepts:

• Clipping: PPO uses a clipped objective function that limits the size of policy updates to prevent overfitting.
• Advantage Estimation: It uses the Generalized Advantage Estimation (GAE) to calculate how much better an action is compared to the average action, helping the agent focus on improving its performance on specific tasks.

The PPO algorithm is well-suited for this task because of its stability and ability to handle continuous action spaces, such as the circuit parameters being optimized in this project.

4.3 Training Process: How the Agent Learns

While the setup section covered the environment initialization and workflow, here we’ll dive deeper into how the agent learns to optimize the circuit.

4.3.1 Environment Interaction

• The agent interacts with the simulation environment by taking actions (modifying circuit parameters), which leads to new circuit configurations.
• After each action, the environment evaluates the circuit’s performance and gives feedback in the form of a reward.
• The agent uses this reward to update its policy, iterating over many episodes to improve its performance.

4.3.2 Learning and Feedback

The agent learns by trial and error, where each circuit modification provides information about whether it leads to better or worse performance. Over time, the agent refines its understanding of which actions result in higher rewards.

4.3.3 Hyperparameters and Tuning

The following hyperparameters play a significant role in the training process:

config_train = {
    "train_batch_size": 1200,  # number of each batch training samples
    "horizon": 30,             # the largest step limitation
    "num_gpus": 0,             # number of used GPU
    "model": {"fcnet_hiddens": [50, 50, 50]},  #hidden neurons 
    "num_workers": 6,          # number of parallel process(cpu)
    "env_config": {"generalize": True, "run_valid": False},
}

By adjusting these hyperparameters, the training process can be fine-tuned to achieve better convergence.

4.4 Monitoring Training Progress

During training, the performance of the agent is tracked using reward curves and training checkpoints. These allow us to visualize the agent’s improvement over time.

• Reward Tracking: This shows the agent’s progress in achieving the desired circuit performance, where higher rewards indicate better performance.
• Checkpoints: The model is periodically saved, allowing the agent to resume training from a specific point.

You can monitor training progress using TensorBoard to visualize metrics like reward and loss.

4.5 Validation: Testing the Trained Agent

Once training is completed, the trained agent is tested on new, unseen design specifications. This validation process ensures that the agent is not just memorizing specific configurations but is capable of generalizing to new circuit designs.

To validate the agent, the following steps are performed:

1. The agent is tested with new design specifications that it hasn’t seen during training.
2. The agent’s ability to achieve the target specifications is assessed by comparing the simulated performance to the expected output.
3. Performance is logged in pickle files, which store the observed results.

4.6 Summary

This section has covered the fundamentals of the RL agent’s training process. The agent learns to optimize circuit parameters using PPO, with the goal of improving the circuit’s performance over time. By interacting with the simulation environment, adjusting parameters, and receiving feedback, the agent iteratively refines its policy. The training process is monitored using reward tracking and training checkpoints, while the final step is to validate the trained agent on new, unseen specifications.