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## Template to run multiple experiments on AdLeap-MAS

##

# 1. Setting the environment

atype = 'pomcp' # choose your method

kwargs = {} # define your additional hyperparameters to it (optional)

env_name = 'LevelForagingEnv' # choose your environment

scenario_id = 5 # define your scenario configuration (check the available configuration in our GitHub)

display = False # choosing to turn on or off the display

NEXP = 50

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# Imports

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import sys

import os

import time

sys.path.append(os.getcwd())

from src.log import LogFile

from src.envs.LevelForagingEnv import load_default_scenario, is_mas

for exp_num in range(NEXP):

print(f'Running experiment {exp_num+1}/{NEXP}...')

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# Setting the environment

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env, scenario_id = load_default_scenario(atype,scenario_id=scenario_id,display=False)

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# ADLEAP-MAS MAIN ROUTINE

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state = env.reset()

agent = env.get_adhoc_agent()

if is_mas(scenario_id):

header = ['Iteration','Reward','Time to reason','N Rollouts', 'N Simulations',

'Coop?']#,'Type Belief']

else:

header = ['Iteration','Reward','Time to reason','N Rollouts', 'N Simulations']

log = LogFile('LevelForagingEnv',scenario_id,atype,exp_num,header)

MAX_EPISODES = 200

done = False

while not done and env.episode < MAX_EPISODES:

print(f'Episode {env.episode}')

# 1. Importing agent method

method = env.import_method(agent.type)

# 2. Reasoning about next action and target

start = time.time()

agent.next_action, _ = method(state, agent)

end = time.time()

# 3. Taking a step in the environment

state,reward,done,info = env.step(agent.next_action)

#if is_mas(scenario_id):

# info['typebelief'] = agent.get_type_belief()

data = {'it':env.episode,

'reward':reward,

'time':end-start,

'nrollout':agent.smart_parameters['count']['nrollouts'],

'nsimulation':agent.smart_parameters['count']['nsimulations'],

'coop':info['coop']}#, 'typebelief':info['typebelief']}

log.write(data)

env.close()

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# THE END - That's all folks :)

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