def experiment(test_game,
num_experiments,
file_name,
num_episodes=500,
alpha=.99,
gamma=.9,
epsilon=.9,
decay_rate=.99):
"""
Main experiment method that runs the Q-Learning experiments and returns prints and draws the needed diagrams.
works by learning a model x number of times and then compiling the number of steps per epoch for experiment
These are then averaged and used to create a graph.
A policy is then also chosen to give an average number of steps needed to reach the goal metric.
"""
list_of_moves_per_experiment = []
policies = []
for x in range(num_experiments):
# Learn model
q_learning = QLearning(test_game,
num_episodes=num_episodes,
alpha=alpha,
gamma=gamma,
epsilon=epsilon,
decay_rate=decay_rate)
q = q_learning.learn()
policies.append(q)
num_moves = q_learning.num_moves_per_episode
list_of_moves_per_experiment.append(num_moves)
np.array(list_of_moves_per_experiment)
moves_per_epoc_number = np.sum(list_of_moves_per_experiment, axis=0)
moves_per_epoc_number = moves_per_epoc_number / num_experiments
# get Average number of steps when executing.
q_learning = QLearning(test_game,
num_episodes=num_episodes,
alpha=alpha,
gamma=gamma,
epsilon=epsilon,
decay_rate=decay_rate)
avg_num_steps = 0
for itter in range(100):
num_steps = q_learning.execute_policy(policies[num_experiments - 1])
avg_num_steps += num_steps[1]
avg_num_steps /= 100.0
generate_validation_curves(np.arange(num_episodes),
moves_per_epoc_number,
None,
"Number of steps",
None,
x_axis_label="Epoc Number",
y_axis_label="Average Path Length",
file_name=file_name)
return avg_num_steps, policies[num_experiments - 1]
from environment import Env
from QLearning import QLearning
if __name__ == "__main__":
env = Env()
QL = QLearning(list(range(env.n_actions)))
for episode in range(1000):
state = env.reset()
while True:
env.render()
# take action and proceed one step in the environment
action = QL.get_action(str(state))
next_state, reward, done = env.step(action)
# with sample <s,a,r,s'>, agent learns new q function
QL.learn(str(state), action, reward, str(next_state))
state = next_state
env.print_value_all(QL.q_table)
# if episode ends, then break
if done:
break
