concurrent actor decisions
parent
6111f69c48
commit
8798de1a83
7
main.py
7
main.py
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@ -8,12 +8,13 @@ STEPS = 1000
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np.random.seed(12345)
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fig, ax = plt.subplots()
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env = Environment((40, 30))
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env = Environment((100, 100))
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first_agent = CAMSReverseAndSidestepAgent(
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environment=env,
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position=(20,15),
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initial_direction=Direction.NORTH
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position=(50,50),
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initial_direction=Direction.NORTH,
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required_resources=300
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)
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im = ax.imshow(env.render(), aspect="equal", origin="lower")
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155
sim.py
155
sim.py
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@ -1,6 +1,7 @@
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import numpy as np
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from enum import Enum, auto
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import matplotlib.pyplot as plt
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from dataclasses import dataclass
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class Direction(Enum):
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NORTH = (0, 1)
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@ -8,10 +9,6 @@ class Direction(Enum):
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WEST = (-1, 0)
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EAST = (1, 0)
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class CellState(Enum):
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OPEN = auto()
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WALL = auto()
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class Colours(Enum):
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WALL = [30,30,30]
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STOCKED = [200,150,0]
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@ -19,23 +16,24 @@ class Colours(Enum):
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UNDEFINED = [255,0,255]
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AGENT = [0,255,0]
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class Cell():
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def __init__(self, state, resources):
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self.state = state
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self.resources = resources
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@dataclass
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class Observations:
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obstacles: np.ndarray
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resources: np.ndarray
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agents: np.ndarray
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def has_obstacle(self, position):
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return self.obstacles[tuple(position)] == 1
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def has_agent(self, position):
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return self.agents[tuple(position)] == 1
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def get_colour(self):
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if self.state is CellState.WALL:
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return Colours.WALL
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elif self.state is CellState.OPEN:
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if self.resources > 0:
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return Colours.STOCKED
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elif self.resources == 0:
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return Colours.DEPLETED
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else:
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return Colours.UNDEFINED
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else:
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return Colours.UNDEFINED
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def has_resources(self, position):
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return self.resources[tuple(position)] > 0
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def get_resources(self, position):
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return self.resources[tuple(position)]
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class Environment:
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def __init__(self, shape):
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@ -46,41 +44,66 @@ class Environment:
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np.random.normal(loc=50, scale=10, size=shape)
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)
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cols, rows = shape
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self.cells = [[] for _ in range(rows)]
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for y in range(rows):
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for x in range(cols):
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if (x == 0 or y == 0 or x+1==cols or y+1==rows):
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self.cells[y].append(Cell(CellState.WALL, 0))
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else:
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self.cells[y].append(Cell(CellState.OPEN, self.resource_map[x,y]))
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self.obstacle_map = np.hstack((
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np.ones((shape[0], 1)),
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np.vstack((
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np.ones((1, shape[1]-2)),
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np.zeros((shape[0]-2, shape[1]-2)),
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np.ones((1, shape[1]-2))
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)),
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np.ones((shape[0], 1))
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))
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print(self.obstacle_map)
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print(self.obstacle_map.shape)
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def step(self):
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agent_map = np.zeros(self.shape)
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for agent in self.agents:
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agent.step()
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agent_map[tuple(agent.position)] = 1
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observations = Observations(
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obstacles=self.obstacle_map,
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resources=self.resource_map,
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agents=agent_map
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)
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for agent in self.agents:
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agent.step(observations)
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def render(self):
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cols, rows = self.shape
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pixel_data = [[] for _ in range(rows)]
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for y in range(self.shape[1]):
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for x in range(self.shape[0]):
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#print((x, y, self.cell((x,y)).state, self.cell((x,y)).get_colour().value))
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pixel_data[y].append(self.cell((x,y)).get_colour().value)
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pixel_data = np.zeros(self.shape + (3,))
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print(pixel_data.shape)
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pixel_data += (
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((self.obstacle_map[..., np.newaxis] == 1) * np.array(Colours.WALL.value))
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+ ((self.obstacle_map[..., np.newaxis] == 0) * (self.resource_map[..., np.newaxis] == 0) * np.array(Colours.DEPLETED.value))
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+ ((self.obstacle_map[..., np.newaxis] == 0) * (self.resource_map[..., np.newaxis] > 0) * np.array(Colours.STOCKED.value))
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)
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print(pixel_data)
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print(pixel_data.shape)
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for agent in self.agents:
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x, y = agent.position
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pixel_data[y][x] = Colours.AGENT.value
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pixel_data[tuple(agent.position)] = agent.get_colour()
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return np.array(pixel_data)
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return np.array(pixel_data).astype(np.uint8).swapaxes(0, 1)
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def eat(self, position):
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resources = self.resource_map[tuple(position)]
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self.resource_map[tuple(position)] = 0
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return resources
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def cell(self, position):
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x, y = tuple(position)
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return self.cells[y][x]
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def is_wall(self, position):
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x, y = tuple(position)
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return self.cells[y][x].state == CellState.WALL
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def has_obstacle(self, position):
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return self.obstacle_map[tuple(position)] == 1
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def has_agent(self, position):
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return tuple(position) in {tuple(agent.position) for agent in self.agents}
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def register_agent(self, agent):
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self.agents.append(agent)
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@ -94,17 +117,22 @@ class Agent:
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self.environment = environment
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self.environment.register_agent(self)
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def step(self):
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def step(self, _observations):
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pass
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def move(self, direction, respect_walls=True):
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def move(self, direction, respect_obstacles=True, respect_agents=True):
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new_position = self.position + direction.value
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if respect_walls and self.environment.is_wall(new_position):
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if respect_obstacles and self.environment.has_obstacle(new_position):
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return False
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elif respect_agents and self.environment.has_agent(new_position):
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return False
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else:
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self.position = new_position
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return True
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def get_colour(self):
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return Colours.AGENT.value
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def die(self):
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self.environment.unregister_agent(self)
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@ -113,16 +141,14 @@ class DirectionalAgent(Agent):
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super().__init__(environment, position)
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self.direction = initial_direction
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def get_cell_in_front(self):
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position_in_front = self.position + self.direction.value
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return self.environment.cell(position_in_front)
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def get_position_in_front(self):
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return self.position + self.direction.value
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def wall_in_front(self):
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position_in_front = self.position + self.direction.value
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return self.environment.is_wall(position_in_front)
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def obstacle_in_front(self, observations):
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return observations.has_obstacle(self.get_position_in_front())
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def agent_in_front(self):
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return False
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def agent_in_front(self, observations):
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return observations.has_agent(self.get_position_in_front())
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def move_forward(self):
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return self.move(self.direction)
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@ -173,12 +199,12 @@ class CAMSReverseAndSidestepAgent(DirectionalAgent):
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self.required_resources = required_resources
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self.number_of_turns = 0
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def step(self):
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def step(self, observations):
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self.eat()
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if self.wall_in_front():
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# self.die()
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#elif self.agent_in_front():
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if self.obstacle_in_front(observations):
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self.die()
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elif self.agent_in_front(observations):
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if self.number_of_turns == 0:
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self.reverse_direction()
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self.move_forward()
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@ -187,12 +213,12 @@ class CAMSReverseAndSidestepAgent(DirectionalAgent):
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self.turn_right()
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self.move_forward()
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self.turn_right()
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self.number_of_turns == 0
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self.number_of_turns = 0
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elif self.resources >= self.required_resources:
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self.resources -= self.required_resources
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new_agent = CAMSReverseAndSidestepAgent(
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environment=self.environment,
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position=(self.position + self.direction.value),
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position=self.position,
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initial_direction=self.direction,
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required_resources=self.required_resources
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)
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@ -201,15 +227,20 @@ class CAMSReverseAndSidestepAgent(DirectionalAgent):
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self.move_forward()
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def eat(self):
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cell = self.environment.cell(self.position)
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resources = cell.resources
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resources = self.environment.eat(self.position)
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if resources > 0:
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self.resources += resources
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cell.resources = 0
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return True
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else:
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return False
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def get_colour(self):
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if self.number_of_turns == 0:
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return [0, 255, 0]
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elif self.number_of_turns == 1:
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return [255, 0, 0]
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else:
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return Colours.UNDEFINED
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