Choosing and Designing Decay Types for Epsilon-Greedy Exploration in Reinforcement Learning

I am working on a reinforcement learning project that involves epsilon-greedy exploration. I have two questions regarding the choice between linear and exponential decay for epsilon, and the appropriate design of the decay constant in the exponential case.

1. How do we determine whether to use linear decay or exponential decay for the epsilon-greedy exploration mechanism in reinforcement learning?
2. When using exponential decay, how should we design the decay constant? Different decay constants determine the shape of the curve. In linear decay, we can use the epsilon_decay parameter to determine the shape, but in exponential decay, the relationship between epsilon_decay and the shape is harder to imagine.

I understand that there may not be a single answer for these questions, and I welcome a wide range of discussions on this topic.

Here is the code for the two types of decay:

import math
import matplotlib.pyplot as plt

def exponential_epsilon_decay(step_idx, epsilon_start=1, epsilon_end=0.01, epsilon_decay=100_000):
"""
Calculates the value of epsilon for a given step index using exponential decay and the specified parameters.

Parameters:
step_idx (int): The index of the current step.
epsilon_start (float): The starting value of epsilon.
epsilon_end (float): The minimum value of epsilon.
epsilon_decay (float): The rate at which epsilon decays.

Returns:
float: The value of epsilon for the given step index.
"""
return epsilon_end + (epsilon_start - epsilon_end) * math.exp(-1. * step_idx / epsilon_decay)

def linear_epsilon_decay(step_idx, epsilon_start=1, epsilon_end=0.01, epsilon_decay=100_000):
"""
Calculates the value of epsilon for a given step index using linear decay and the specified parameters.

Parameters:
step_idx (int): The index of the current step.
epsilon_start (float): The starting value of epsilon.
epsilon_end (float): The minimum value of epsilon.
epsilon_decay (float): The total number of steps over which epsilon will decay from epsilon_start to epsilon_end.

Returns:
float: The value of epsilon for the given step index.
"""
return epsilon_end + (epsilon_start - epsilon_end) * max((1 - step_idx / epsilon_decay), 0)

n_steps = 1_000_000
epsilon_decay_rates = [20_000, 100_000, 500_000]

# Calculate epsilon values for each step
exp_epsilon_values = [[exponential_epsilon_decay(step, epsilon_decay=epsilon_decay_rate) for step in range(n_steps)] for epsilon_decay_rate in epsilon_decay_rates]
lin_epsilon_values = [[linear_epsilon_decay(step, epsilon_decay=epsilon_decay_rate) for step in range(n_steps)] for epsilon_decay_rate in epsilon_decay_rates]

# Plot the epsilon decay
plt.plot(exp_epsilon_values[0], 'r-', label='Exp decay_rate=20_000')
plt.plot(exp_epsilon_values[1], 'g-', label='Exp decay_rate=100_000')
plt.plot(exp_epsilon_values[2], 'b-', label='Exp decay_rate=500_000')
plt.plot(lin_epsilon_values[0], 'r--', label='Lin decay_rate=20_000')
plt.plot(lin_epsilon_values[1], 'g-.', label='Lin decay_rate=100_000')
plt.plot(lin_epsilon_values[2], 'b:', label='Lin decay_rate=500_000')
plt.title('Epsilon Decay')
plt.xlabel('Step')
plt.ylabel('Epsilon')
plt.legend()
plt.show()

In linear_epsilon_decay, the epsilon_decay parameter corresponds to the moment when the decay reaches epsilon_end, but in exponential_epsilon_decay, the epsilon_decay parameter has a less direct relationship with when it reaches epsilon_end.

Feel free to point out any issues in the code, and I appreciate any insights or suggestions you can provide.

• Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
– Community Bot
Apr 3, 2023 at 14:44