"""Built-in naive rule-based agents."""
import numpy as np
from mate.agents.base import CameraAgentBase, TargetAgentBase
from mate.constants import NUM_WAREHOUSES, WAREHOUSE_RADIUS, WAREHOUSES
__all__ = ['NaiveCameraAgent', 'NaiveTargetAgent']
[docs]class NaiveCameraAgent(CameraAgentBase):
"""Naive Camera Agent
Rotates anti-clockwise with the maximum viewing angle.
"""
[docs] def act(self, observation, info=None, deterministic=None):
"""Get the agent action by the observation.
This function will be called before every env.step().
Rotate anti-clockwise with the maximum viewing angle.
"""
self.state, observation, info, _ = self.check_inputs(observation, info)
action = self.np_random.uniform(0.0, 0.4) * self.action_space.high
return action
[docs]class NaiveTargetAgent(TargetAgentBase):
"""Naive Target Agent
Visits all warehouses in turn.
"""
[docs] def __init__(self, seed=None):
"""Initialize the agent.
This function will be called only once on initialization.
"""
super().__init__(seed=seed)
self.goal = 0
self.prev_state = None
self.prev_noise = None
self.inc = +1
@property
def goal_location(self):
"""Location of the current warehouse."""
return WAREHOUSES[self.goal]
[docs] def reset(self, observation):
"""Reset the agent.
This function will be called immediately after env.reset().
"""
super().reset(observation)
self.prev_state = self.state
self.prev_noise = 0.5 * self.action_space.sample()
self.goal = self.np_random.choice(NUM_WAREHOUSES)
self.inc = self.np_random.choice([+1, -1])
[docs] def act(self, observation, info=None, deterministic=None):
"""Get the agent action by the observation.
This function will be called before every env.step().
Visit all warehouses in turn.
"""
self.state, observation, info, _ = self.check_inputs(observation, info)
if np.linalg.norm(self.state.location - self.goal_location) <= 0.9 * WAREHOUSE_RADIUS:
if self.state.goal_bits.any() or self.state.empty_bits.all():
self.goal = (self.goal + self.inc) % NUM_WAREHOUSES
else:
while True:
self.goal = (self.goal + self.inc) % NUM_WAREHOUSES
if not self.state.empty_bits[self.goal]:
break
prev_actual_action = self.state.location - self.prev_state.location
action = self.goal_location - self.state.location
step_size = np.linalg.norm(action)
if step_size > self.state.step_size:
action *= self.state.step_size / step_size
prob = 0.05 if np.linalg.norm(prev_actual_action) > 0.2 * self.state.step_size else 0.75
if self.np_random.binomial(1, prob) != 0:
noise = 0.5 * self.action_space.sample()
else:
noise = self.prev_noise
action = (action + noise).clip(min=self.action_space.low, max=self.action_space.high)
self.prev_state = self.state
self.prev_noise = noise
return action