Understanding Generalized Advantage Estimation In Reinforcement Learning

Introduction

In reinforcement learning (RL), an agent learns to make optimal decisions by interacting with an environment. One of the key concepts in RL is the advantage function that measures how much better an action is compared to the average action at a state. There are several ways to estimate the advantage function, and one of the popular methods is called the Generalized Advantage Estimation (GAE).

What is Generalized Advantage Estimation?

GAE, introduced by John Schulman et al., in their 2015 paper, is a technique that aims to reduce the variance of the advantage function estimation while maintaining a reasonable level of bias. It leverages a parameter λ to mix up multiple-step returns to get a better estimate of the advantage function.

The Generalized Advantage Estimation Equation is defined as follows:

In this equation, γ represents a discount rate, λ is a parameter, and V refers to the value estimation.

Estimating GAE in Python

Here's a Python function that computes the GAE given rewards, values and the two hyperparameters γ and λ:

import numpy as np

def compute_gae(next_value, rewards, masks, values, gamma=0.99, lam=0.95):
    values = values + [next_value]
    gae = 0
    returns = []
    for step in reversed(range(len(rewards))):
        delta = rewards[step] + gamma * values[step + 1] * masks[step] - values[step]
        gae = delta + gamma * lam * masks[step] * gae
        returns.insert(0, gae + values[step])
    return returns

In this function, next_value is the value of the next state, rewards is a list of rewards over multiple steps, masks is a list indicating if a sequence has ended at a step (1 if not ended, 0 if ended), and values is a list of value estimations over multiple steps.

Conclusion

To sum up, Generalized Advantage Estimation is a critical tool in reinforcement learning that estimates the advantage function efficiently. It strikes a sweet balance between bias and variance, helping the RL algorithms to converge faster.

Please remember that appropriate setting of γ and λ can significantly impact your model's learning efficiency and final performance in the Reinforcement Learning setting.

It's important to keep exploring different techniques and methods in the vast and fascinating field of Artificial Intelligence!