def l4_planning(env, agent): # 1. Choosing a target if agent.target is None or env.state[agent.target[0],agent.target[1]] == -1: # - choosing a target target_position = l4_choose_target(env.state) agent.target = target_position else: target_position = agent.target # - planning the action/route to the target # if it exists if target_position is not None: next_action = a_star_planning(env.state, env.state.shape[0], env.state.shape[1], env.action_space, agent.position, target_position) # else, take a random action else: next_action = env.action_space.sample() # 2. Verifying if the agent can complete a task if agent.direction == np.pi/2: pos = (agent.position[0],agent.position[1]+1) elif agent.direction == 3*np.pi/2: pos = (agent.position[0],agent.position[1]-1) elif agent.direction == 0: pos = (agent.position[0]+1,agent.position[1]) elif agent.direction == np.pi: pos = (agent.position[0]-1,agent.position[1]) if pos == target_position: #target_position = None agent.target = target_position return 4, target_position return next_action,target_position
def c1_planning(env, agent, mode='spatial'):
action, target_pos = None, None
# checking if the agent already chosen a prey
if (agent.target):
for t in env.components['tasks']:
if (t.index == agent.target):
# if it is completed, pursue a new one
if (t.completed):
agent.target = choose_target(env, agent, mode)
target_pos = agent.target_position
# else, keep going
else:
target_pos = t.position
# defining the path to the prey
if (target_pos):
agent.target_position = target_pos
action = a_star_planning(env.state, env.state.shape[0],
env.state.shape[1], env.action_space,
agent.position, agent.target_position)
return action, agent.target
else:
action = a_star_planning(env.state, env.state.shape[0],
env.state.shape[1], env.action_space,
agent.position, agent.target_position)
return action, agent.target
# else, choose one
else:
agent.target = choose_target(env, agent, mode)
# if did not find a valid target, move randomly
if (not agent.target):
action = env.action_space.sample()
return action, None
# else pursue it
else:
action = a_star_planning(env.state, env.state.shape[0],
env.state.shape[1], env.action_space,
agent.position, agent.target_position)
return action, agent.target