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rl/policy.py
Epsilon-greedy policy for exploration during training.
"""
from __future__ import annotations
import random
import torch
from drone_env.rl.model import PathQNet, ACTIONS
class EpsilonGreedyPolicy:
"""
Selects actions using epsilon-greedy strategy.
Epsilon decays linearly from eps_start → eps_end over decay_steps.
"""
def __init__(self, eps_start: float = 1.0, eps_end: float = 0.05, decay_steps: int = 5000):
self.eps = eps_start
self.eps_end = eps_end
self.decay = (eps_start - eps_end) / max(decay_steps, 1)
self.step_count = 0
def select_action(self, q_values: torch.Tensor) -> int:
if random.random() < self.eps:
return random.randint(0, len(ACTIONS) - 1)
return int(q_values.argmax().item())
def decay_epsilon(self):
self.step_count += 1
self.eps = max(self.eps_end, self.eps - self.decay)
@property
def current_epsilon(self) -> float:
return self.eps
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