Reinforcement Learning: PPO in Just 100 Lines of code

Master Proximal Policy Optimization from Scratch and Take Your Reinforcement Learning Models to the Next Level

Are you looking to deepen your understanding of reinforcement learning (RL) and take your models to new heights? Proximal Policy Optimization (PPO) is one of the most powerful algorithms in modern RL, balancing efficiency and performance. In this comprehensive guide, we’ll break down a full implementation of PPO, helping you grasp the intricacies of the algorithm and how to apply it effectively.

An agent playing Breakout after training with PPO | Reinforcement Learning Tutorial

Introduction

Proximal Policy Optimization has emerged as a cornerstone in reinforcement learning due to its robustness and simplicity. By preventing drastic policy updates, PPO ensures stable and efficient learning, making it a go-to choice for many practitioners. This tutorial will walk you through a detailed PPO implementation, explaining each component and showing how they fit together to train an agent successfully.

Proximal Policy Optimization Algorithms

In this tutorial, we are going to mainly focus on the implementation of the PPO paper. If you need more details about the maths and mechanisms behind the algorithm, I have a full course about reinforcement learning on Udemy.

Atari Breakout Game

Proximal Policy Optimization: Implementation

Let’s dive into the heart of the implementation: the PPO function.

Hyper-parameters | PPO from Scratch in PyTorch

Hyper-parameters Breakdown

• envs: A collection of environments running in parallel, providing diverse experiences.
• T (Horizon Length): Number of timesteps per rollout per environment.
• K: Number of epochs during training.
• batch_size: Size of the minibatches for stochastic gradient descent.
• gamma (Discount Factor): Determines the importance of future rewards.
• device: Computing device (‘cuda’ for GPU acceleration).
• gae_parameter (Lambda for GAE): Balances bias and variance in advantage estimation.
• vf_coeff_c1: Coefficient for the value function loss term.
• ent_coef_c2: Coefficient for the entropy bonus, encouraging exploration.
• nb_iterations: Total number of training iterations.

Proximal Policy Optimization: Main Training Loop

The core of the algorithm lies in the main training loop, which orchestrates data collection, advantage estimation, and policy optimization.

Loop Structure

for iteration in tqdm(range(nb_iterations)):
    # Initialize buffers and collect data
    # Compute advantages using GAE
    # Periodically plot rewards
    # Optimize the policy and value network
    # Update the learning rate scheduler

PPO: Data Collection and Experience Storage

Efficient data collection is crucial for training robust models.

Initializing Buffers

Buffers | Implementing PPO in 100 Lines of Code

Purpose of Buffers: Store experiences from each environment for T timesteps, including states, actions, rewards, and other relevant information.

PPO:Collecting Experiences

Data Collection in PPO Algorithm | Reinforcement Learning

Processing Observations and Selecting Actions

Reinforcement Learning Techniques with PPO

• Observation Preprocessing: Normalizes pixel values and converts observations to tensors.
• Policy and Value Evaluation: Obtains action probabilities (logits) and state value estimates.
• Action Sampling: Chooses an action based on the current policy.

Interacting with the Environment

• Logging Probabilities: Essential for calculating the policy loss.
• Environment Step: Executes the selected action.
• Reward Clipping: Stabilizes training by normalizing rewards to -1, 0, or 1.

Storing Experiences

• Buffer Updates: Collects all necessary data for training.

Handling Episode Terminations and Model Saving

Step-by-Step Guide to PPO Implementation | Reinforcement Learning

• Model Checkpointing: Saves the model when a new maximum reward is achieved.
• Environment Reset: Prepares the environment for a new episode.

PPO: Advantage Estimation with GAE

Generalized Advantage Estimation provides a balance between bias and variance.

Calculating Advantages

Generalized Advantage Estimation (GAE) Tutorial | Reinforcement Learning

• Temporal Difference (TD) Error: Measures the discrepancy between predicted and actual rewards.
• Recursive Advantage Calculation: Incorporates future advantages to refine current estimates.

PPO: Policy and Value Network Optimization

After collecting experiences and calculating advantages, we proceed to optimize the policy.

Preparing the Data Loader

Data Loader | Proximal Policy Optimization (PPO) Tutorial in PyTorch

• Data Loader Setup: Organizes data into batches for efficient training.

Optimization Loop

Optimization Loop Structure in Proximal Policy Optimization (PPO)

Calculating Policy Loss with Clipping

PPO: Policy Loss with Clipping

• Probability Ratio (r): Indicates how the policy has changed.
• Clipping: Prevents large updates that could destabilize training.

Calculating Value Function Loss with Clipping

PPO: Value Loss with Clipping

• Value Loss Clipping: Ensures stable updates to the value function.

Adding Entropy Regularisation and Computing Total Loss

PPO: Entropy Regularisation

• Entropy Bonus: Encourages the policy to remain stochastic, promoting exploration.
• Total Loss: Combines policy loss, value loss, and entropy bonus.

Backpropagation and Parameter Updates

Reinforcement Learning Tutorial with PPO Algorithm

• Gradient Clipping: Prevents exploding gradients.
• Optimizer Step: Updates the network parameters.

Periodic Reward Plotting

Visualizing progress helps in monitoring the training process.

• Plotting Frequency: Every 400 iterations, the average rewards are plotted.
• Clearing Rewards: Resets the total rewards for the next interval.

Neural Network Architecture: Shared Weights between Actor and Critic

An essential part of our PPO implementation is the neural network architecture used for both the policy (actor) and the value function (critic). In our code, the ActorCritic class defines this architecture, where the actor and critic share the initial layers (weights) for feature extraction.

Shared Feature Extractor in Actor-Critic Networks | PPO

Shared Feature Extractor

• Head Network (self.head): This is a shared feature extractor comprising convolutional layers followed by a fully connected layer. It processes the input observations and learns a rich representation of the environment.
• Convolutional Layers: Capture spatial features from input images (e.g., frames from a game).
• Activation Functions: We use Tanh activations to introduce non-linearity.
• Flatten and Fully Connected Layer: Transforms the output of convolutional layers into a 1D feature vector.

Separate Output Layers

• Actor Network (self.actor): Takes the shared features h and outputs action logits, which are used to sample actions.
• Output Layer: A linear layer mapping to the number of possible actions (nb_actions).
• Critic Network (self.critic): Also receives the shared features h but outputs a single value estimate representing the state's value.
• Output Layer: A linear layer producing a scalar value.

Benefits of Shared Weights

• Efficient Learning: By sharing the feature extractor, both the actor and critic learn from the same representations, which can accelerate training.
• Reduced Parameters: Sharing layers reduces the total number of parameters, making the model more memory-efficient.
• Consistent Representations: Ensures that both networks are aligned in their understanding of the environment.

Incorporating a shared architecture aligns with the principles of actor-critic methods, where the policy and value function often benefit from learning similar features. This design choice simplifies the network and can lead to improved performance in training reinforcement learning agents.

Conclusion

By dissecting this PPO implementation, we’ve explored how each component — from data collection to policy optimization — works together to train an effective reinforcement learning agent. Understanding these details empowers you to modify and extend the algorithm for your specific needs.

Proximal Policy Optimization remains a powerful tool in the RL practitioner’s arsenal. With this knowledge, you’re well-equipped to implement PPO in your projects and push the boundaries of what’s possible in reinforcement learning.

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