def main(args):
"""Main Program."""
problem_ids, episodes, grid = parse_args(args)
print('It was found out that setting the seed for random was slow.. you can turn it on with seed=True')
print('More info in documentation...')
# Reset the random generator to a known state (for reproducability)
np.random.seed(12)
for problem_id in problem_ids:
# this seed doesn't work... if needed, change seed to True below
agent = RandomAgent(problem_id=problem_id, map_name_base=grid)
agent.solve(episodes=episodes, seed=None)
agent.evaluate(episodes)
def main(args):
print(args)
problem_ids, episodes, grid = parse_args(args)
for problem_id in problem_ids:
# this seed doesn't work... if needed, change seed to True below
random_agent = RandomAgent(problem_id=problem_id, map_name_base=grid)
random_agent.solve(episodes=episodes, seed=None)
random_agent.evaluate(episodes)
simple_agent = SimpleAgent(problem_id=problem_id, map_name_base=grid)
simple_agent.solve(episodes=episodes)
simple_agent.evaluate(episodes)
rl_agent = ReinforcementLearningAgent(problem_id=problem_id,
map_name_base=grid)
rl_agent.solve(episodes=episodes)
rl_agent.evaluate(episodes)
passive_agent = PassiveAgent(problem_id=problem_id, map_name_base=grid)
passive_agent.solve()
passive_agent.evaluate(episodes)
# Adding the plots for evaluation
labels = ['Episodes', 'Mean Reward']
agents = {
'random': random_agent,
'simple': simple_agent,
'rl': rl_agent
}
title = 'Problem {}. Episodes vs Mean Reward Plot'.format(problem_id)
filename = '{}_{}_first_1000_training'.format(problem_id,
random_agent.env.ncol)
subtitle = 'First 1000 Episodes vs Mean Reward (Training Phase)'
plot_eval(agents, range(999), labels, filename, title, subtitle)
filename = '{}_{}_training'.format(problem_id, random_agent.env.ncol)
subtitle = 'Episodes Number vs Mean Reward (Training Phase)'
plot_eval(agents, range(episodes), labels, filename, title, subtitle)
filename = '{}_{}_first_1000_evaluation'.format(
problem_id, random_agent.env.ncol)
subtitle = 'First 1000 episodes vs Mean Reward (Evaluation Phase)'
plot_eval(agents,
range(999),
labels,
filename,
title,
subtitle,
training=False)
filename = '{}_{}_evaluation'.format(problem_id, random_agent.env.ncol)
subtitle = 'Episodes Number vs Mean Reward (Evaluation Phase)'
plot_eval(agents,
range(episodes),
labels,
filename,
title,
subtitle,
training=False)
compare_utils(passive_agent.U, rl_agent.U)
