def run_Q_learning(seed,
epsilon_current=0.1,
max_episodes=10000,
epsilon_decrease=1.,
start_at_bottom=False):
env = MountainCarWithResetEnv()
np.random.seed(seed)
env.seed(seed)
gamma = 0.999
learning_rate = 0.05
epsilon_min = 0.05
solver = Solver(
# learning parameters
gamma=gamma,
learning_rate=learning_rate,
# feature extraction parameters
number_of_kernels_per_dim=[7, 5],
# env dependencies (DO NOT CHANGE):
number_of_actions=env.action_space.n,
)
bottom_state = np.asarray([-0.5, 0])
bottom_state_val = []
success_rates = []
episodes_gain = []
episodes_bellman_err = []
for episode_index in range(1, max_episodes + 1):
episode_gain, mean_delta = run_episode(env,
solver,
is_train=True,
epsilon=epsilon_current,
start_at_bottom=start_at_bottom)
episodes_gain.append(episode_gain)
# reduce epsilon if required
epsilon_current *= epsilon_decrease
epsilon_current = max(epsilon_current, epsilon_min)
episodes_bellman_err.append(mean_delta)
bottom_state_features = solver.get_features(bottom_state)
bottom_state_max_action = solver.get_max_action(bottom_state)
bottom_state_val.append(
solver.get_q_val(bottom_state_features, bottom_state_max_action))
# termination condition:
if episode_index % 10 == 9:
test_gains = [
run_episode(env, solver, is_train=False, epsilon=0.)[0]
for _ in range(10)
]
mean_test_gain = np.mean(test_gains)
success_rates.append(np.mean(np.asarray(test_gains) > -200))
print(f'tested 10 episodes: mean gain is {mean_test_gain}')
if mean_test_gain >= -75.:
print(f'solved in {episode_index} episodes')
break
return episodes_gain, success_rates, bottom_state_val, episodes_bellman_err
def __init__(self):
self.game = MountainCarWithResetEnv()
self.reset_theta()
# Constants used for data standardization
self.pos_mu = (self.game.min_position + self.game.max_position)/2
self.pos_sigma = (self.game.max_position - self.game.min_position)/np.sqrt(12)
self.speed_mu = 0
self.speed_sigma = 2*self.game.max_speed/np.sqrt(12)
# Cache of samples used for visualizing the policy
self.vis_samples = None
def mean_std_of_states():
env = MountainCarWithResetEnv()
samples_to_collect = 100000
states, actions, rewards, next_states, done_flags = DataCollector(env).collect_data(samples_to_collect)
all_states = np.concatenate((states, next_states))
states_mean = np.mean(all_states, axis=0)
states_std = np.std(all_states, axis=0)
print("states_mean: {}, states_std: {}".format(states_mean, states_std))
def __discretize_state_space(n: int = 100):
states = MountainCarWithResetEnv().observation_space
positions = np.linspace(states.low[0], states.high[0], n)
velocities = np.linspace(states.low[1], states.high[1], n)
position_v, velocity_v = np.meshgrid(positions, velocities, sparse=False, indexing='ij')
return position_v, velocity_v, n
def training_the_model(samples_to_collect=100000, seed=100):
number_of_kernels_per_dim = [10, 8]
gamma = 0.999
w_updates = 20
evaluation_number_of_games = 50
evaluation_max_steps_per_game = 300
np.random.seed(seed)
env = MountainCarWithResetEnv()
# collect data
states, actions, rewards, next_states, done_flags = DataCollector(
env).collect_data(samples_to_collect)
# get data success rate
data_success_rate = np.sum(rewards) / len(rewards)
print(f'Data Success Rate {data_success_rate}')
# standardize data
data_transformer = DataTransformer()
data_transformer.set_using_states(
np.concatenate((states, next_states), axis=0))
states = data_transformer.transform_states(states)
next_states = data_transformer.transform_states(next_states)
# process with radial basis functions
feature_extractor = RadialBasisFunctionExtractor(number_of_kernels_per_dim)
# encode all states:
encoded_states = feature_extractor.encode_states_with_radial_basis_functions(
states)
encoded_next_states = feature_extractor.encode_states_with_radial_basis_functions(
next_states)
# set a new linear policy
linear_policy = LinearPolicy(feature_extractor.get_number_of_features(), 3,
True)
# but set the weights as random
linear_policy.set_w(np.random.uniform(size=linear_policy.w.shape))
# start an object that evaluates the success rate over time
evaluator = GamePlayer(env, data_transformer, feature_extractor,
linear_policy)
success_rate_vs_iteration = list()
for lspi_iteration in range(w_updates):
print(f'Starting LSPI iteration {lspi_iteration}')
new_w = compute_lspi_iteration(encoded_states, encoded_next_states,
actions, rewards, done_flags,
linear_policy, gamma)
norm_diff = linear_policy.set_w(new_w)
success_rate = evaluator.play_games(evaluation_number_of_games,
evaluation_max_steps_per_game)
success_rate_vs_iteration.append(success_rate)
if norm_diff < 0.00001:
break
print('LSPI Done')
return success_rate_vs_iteration
def lspi_data_sample(N=3000):
env = MountainCarWithResetEnv()
goal_pos = 0.5
min_pos = -1.2
max_pos = 0.6
min_speed = -0.07
max_speed = 0.07
data = []
rewards = np.zeros([N, 1])
states = np.zeros([N, 2])
actions = np.zeros(N)
next_states = np.zeros([N, 2])
for i in range(N):
#for pos in np.linspace(min_pos, max_pos, num=N_pos):
#for speed in np.linspace(min_speed, max_speed, num=N_speed):
#for action in [0, 1, 2]:
pos = (max_pos - min_pos) * np.random.sample() + min_pos
speed = (max_speed - min_speed) * np.random.sample() + min_speed
action = np.random.choice(3)
#res = {'s' : np.array([pos, speed]), 'a' : action}
states[i, :] = np.array([pos, speed])
actions[i] = action
if pos >= goal_pos:
#res['r'] = 1
rewards[i, 0] = 1
#res['s_next'] = np.array([pos, speed])
next_states[i, :] = np.array([pos, speed])
else:
env.reset_specific(pos, speed)
s_next, reward, _, _ = env.step(action)
#res['r'] = reward
rewards[i, 0] = reward
#res['s_next'] = s_next
next_states[i, :] = s_next
return data, states, actions, rewards, next_states
def test_lspi(N_list=[3000]):
results = []
fig, ax = plt.subplots()
#plt.rcParams.update({'font.size': 10})
for N in N_list:
N = int(N)
env = MountainCarWithResetEnv()
high = -0.4
low = -0.6
init_states = [((high - low) * np.random.sample() + low, 0)
for i in range(10)]
max_iter = 1000
total_success = 5 * [[]]
for i in range(5):
#print("Starting iteration i=", i)
np.random.seed(seed=i)
data, states, actions, rewards, next_states = lspi_data_sample(N)
theta_n = list(
train_lspi(data, states, actions, rewards, next_states))
success_theta = []
for theta in theta_n:
#print("New theta")
success_rate = 0
for init_s in init_states:
#print("New init state")
env.reset_specific(*init_s)
#env.render()
is_done = False
a = next_a(np.array(init_s).reshape([1, 2]),
theta) # First step
for j in range(max_iter):
#print("Game iteration:", j)
next_s, r, is_done, _ = env.step(int(a))
a = next_a(next_s.reshape([1, 2]), theta)
if is_done:
success_rate += 1.0
break
success_theta.append(success_rate / 10.0)
total_success[i] = success_theta
max_len = max(
[len(total_success[i]) for i in range(len(total_success))])
for i in range(len(total_success)):
l = len(total_success[i])
if l < max_len:
for j in range(max_len - l):
total_success[i].append(total_success[i][-1])
res = np.array(total_success)
res_mean = np.mean(res, axis=0)
results.append(list(res_mean))
max_len = max([len(results[i]) for i in range(len(results))])
for i in range(len(results)):
l = len(results[i])
if l < max_len:
for j in range(max_len - l):
results[i].append(results[i][-1])
for N, res_mean in zip(N_list, results):
it = list(range(1, len(res_mean) + 1))
ax.plot(it, res_mean, label='N = ' + str(int(N)))
ax.grid(True)
if len(N_list) > 1:
plt.title(
'Average success rate per iteration for different amounts of samples'
)
else:
plt.title('Average success rate per iteration')
plt.xlabel('Iteration')
plt.ylabel('Success rate')
plt.legend(loc='lower left')
plt.ylim(-0.2, 1.2)
plt.show()
return res_mean
plt.xlabel('Position', fontsize=fsize)
plt.ylabel('Velocity', fontsize=fsize)
plt.show()
#if __name__ == '__main__':
# import sys
# if len(sys.argv) > 1:
# N = int(sys.argv[1])
# print(test_lspi(N))
# else:
# print(test_lspi())
# %%
if __name__ == '__main__':
env = MountainCarWithResetEnv()
# # run no force
# env.reset()
# env.render()
# is_done = False
# while not is_done:
# _, r, is_done, _ = env.step(1)
# env.render()
# print(r)
# # run random forces
# env.reset()
# env.render()
# is_done = False
# while not is_done:
# _, r, is_done, _ = env.step(env.action_space.sample()) # take a random action
# env.render()
if __name__ == '__main__':
samples_to_collect = 100000
# samples_to_collect = 150000
# samples_to_collect = 10000
number_of_kernels_per_dim = [10, 8]
gamma = 0.99
w_updates = 100
evaluation_number_of_games = 10
evaluation_max_steps_per_game = 1000
np.random.seed(123)
# np.random.seed(234)
env = MountainCarWithResetEnv()
# collect data
states, actions, rewards, next_states, done_flags = DataCollector(
env).collect_data(samples_to_collect)
# get data success rate
data_success_rate = np.sum(rewards) / len(rewards)
print(f'success rate {data_success_rate}')
# standardize data
data_transformer = DataTransformer()
data_transformer.set_using_states(
np.concatenate((states, next_states), axis=0))
states = data_transformer.transform_states(states)
next_states = data_transformer.transform_states(next_states)
# process with radial basis functions
feature_extractor = RadialBasisFunctionExtractor(number_of_kernels_per_dim)
# encode all states:
if __name__ == "__main__":
seeds = [123]
epsilons = [1]
gamma = 0.999
learning_rate = 0.05
epsilon_decrease = 0.99
epsilon_min = 0.01
max_episodes = 10000
seed_rewards, seed_performance, seed_bottom_val, seed_bellman_err_avg = [], [], [], []
for seed in seeds:
env = MountainCarWithResetEnv()
np.random.seed(seed)
env.seed(seed)
solver = Solver(
# learning parameters
gamma=gamma,
learning_rate=learning_rate,
# feature extraction parameters
number_of_kernels_per_dim=[5, 7],
# env dependencies (DO NOT CHANGE):
number_of_actions=env.action_space.n,
)
for epsilon_current in epsilons:
rewards, performance, bottom_val, bellman_err_avg, bellman_err = [], [], [], [], []
# action = int(np.sign(state[1]) + 1)
# w=np.array([-0.1,0,0,0.1,0.1,0])
action = self.greedy_policy(state, [False])[0]
state, reward, done, _ = env.step(action)
rewards += reward
if done or itr > 10e2:
break
itr += 1
rew.append(rewards)
return np.mean(rew), np.std(rew)
if __name__ == '__main__':
env = MountainCarWithResetEnv()
# # run no force
# env.reset()
# env.render()
# is_done = False
# while not is_done:
# _, r, is_done, _ = env.step(1)
# env.render()
# print(r)
# # run random forces
# env.reset()
# env.render()
# is_done = False
# while not is_done:
# _, r, is_done, _ = env.step(env.action_space.sample()) # take a random action
# env.render()
class QLearningAgent:
def __init__(self):
self.game = MountainCarWithResetEnv()
self.reset_theta()
# Constants used for data standardization
self.pos_mu = (self.game.min_position + self.game.max_position)/2
self.pos_sigma = (self.game.max_position - self.game.min_position)/np.sqrt(12)
self.speed_mu = 0
self.speed_sigma = 2*self.game.max_speed/np.sqrt(12)
# Cache of samples used for visualizing the policy
self.vis_samples = None
def reset_theta(self):
self.theta = np.random.normal(size=(1, 78))
def reset(self, state=None):
if state is None:
return self.game.reset()
return self.game.reset_specific(*state)
def next_a(self, state):
N = np.shape(state)[0]
Q_est = np.zeros([N, 3])
Q_est[:, 0] = self.theta.dot(self.extract_features(state, np.zeros(N)).T)
Q_est[:, 1] = self.theta.dot(self.extract_features(state, np.ones(N)).T)
Q_est[:, 2] = self.theta.dot(self.extract_features(state, 2*np.ones(N)).T)
action = np.argmax(Q_est, axis=1)
return 2 - action
def q_max(self, state):
N = np.shape(state)[0]
Q_est = np.zeros([N, 3])
Q_est[:, 0] = self.theta.dot(self.extract_features(state, np.zeros(N)).T)
Q_est[:, 1] = self.theta.dot(self.extract_features(state, np.ones(N)).T)
Q_est[:, 2] = self.theta.dot(self.extract_features(state, 2*np.ones(N)).T)
return np.max(Q_est, axis=1)
def q(self, state, action):
Q_est = self.theta.dot(self.extract_features(state, action * np.ones(1)).T)
return Q_est
def extract_features(self, s, actions):
N_a = 3
e_s = self.rbf(s)
N_f = np.shape(e_s)[1]
feats = np.zeros([np.shape(e_s)[0], N_a * N_f])
for i, a in enumerate(actions):
np.put(feats[i, :], range(int(a)*N_f, int(a+1)*N_f), e_s[i, :])
return feats
def rbf(self, s):
# Implementation of RBF features
# pos, speed statistics should be global
n_s = np.zeros(s.shape)
n_s[:, 0] = (s[:, 0] - self.pos_mu) / self.pos_sigma
n_s[:, 1] = (s[:, 1] - self.speed_mu) / self.speed_sigma
centers = []
for i in -1.2, -0.6, 0, 0.6, 1.2:
for j in -0.07, -0.03, 0, 0.03, 0.07:
centers.append((i, j))
n_centers = np.array([(
(c[0] - self.pos_mu)/self.pos_sigma,
(c[1] - self.speed_mu)/self.speed_sigma) for c in centers])
scales = [1 for c in centers]
feats = np.ones([n_s.shape[0], np.size(scales) + 1])
for i, n_c in enumerate(n_centers):
feats[:, i] = np.exp(-scales[i] * np.linalg.norm(n_s - n_c, axis=1))
return feats
def visualize(self):
if self.vis_samples is None:
ret = lspi_data_sample(10000)
self.vis_samples = ret[1]
N = self.vis_samples.shape[0]
opt_a = self.next_a(self.vis_samples)
plt.clf()
ac = [0, 1, 2]
for a, color, label in zip(ac, ['tab:blue', 'tab:orange', 'tab:green'], ['LEFT', 'STAY', 'RIGHT']):
xy = self.vis_samples[a == opt_a, :]
plt.scatter(xy[:, 0], xy[:, 1], c=color, label=label, edgecolors='none')
plt.legend()
plt.grid(True)
plt.title('Sample size - {}'.format(N))
plt.xlabel('Position')
plt.ylabel('Velocity')
plt.pause(0.1)
def gather_data(self, epsilon, iterations_per_game=1000, games=5):
states = np.zeros((iterations_per_game*games, 2))
actions = np.zeros((iterations_per_game*games, 1))
next_states = np.zeros((iterations_per_game*games, 2))
rewards = np.zeros((iterations_per_game*games, 1))
data = (states, actions, next_states, rewards)
success_count = 0
data_index = 0
for g in range(games):
state = self.reset()
state = state.reshape((1, 2))
for i in range(iterations_per_game):
if np.random.uniform() > epsilon:
rand = False
action = self.next_a(state)[0]
else:
rand = True
action = np.random.choice(3)
next_state, reward, is_done, _ = self.game.step(action)
success_count += np.sum(reward)
states[data_index, :] = state
actions[data_index, :] = action
next_states[data_index, :] = next_state
rewards[data_index, :] = reward
data_index += 1
#print("i: {}, state: {}, action: {}, next: {}, r: {}, rand: {}".format(i, state, action, next_state, reward, rand))
state = np.array(next_state).reshape((1, 2))
if is_done:
break
success_rate = success_count / games
return data, success_rate, data_index
def train_step(self, alpha, data, batch_size=100, gamma=0.999):
data_length = data[0].shape[0]
reward_indices = (data[3] == 1).reshape(data_length)
reward_count = reward_indices.sum()
batch_indices = np.random.randint(0, data_length, batch_size - reward_count)
batch_marker = np.zeros(data_length, dtype=bool)
batch_marker[batch_indices] = True
batch_marker[reward_indices] = True
batch_size = batch_marker.sum()
states = data[0][batch_marker]
actions = data[1][batch_marker]
next_states = data[2][batch_marker]
rewards = data[3][batch_marker]
update_step = 0
for i in range(batch_size):
coeff = rewards[i] + gamma * self.q_max(next_states[i].reshape((1, 2))) - self.q(states[i].reshape((1, 2)), actions[i])
step = self.extract_features(states[i].reshape((1, 2)), actions[i]) * coeff
update_step += step
max_element = np.max(np.abs(update_step))
return self.theta + alpha * update_step / (max_element or 1)
def reset_random(self):
init_state = (np.random.uniform(-1.2, 0.6), np.random.uniform(-0.07, 0.07))
return self.reset(init_state)
def train(self, init_epsilon=1, init_alpha=1, max_iterations=30, visualise=True, test_states=[]):
alpha = init_alpha
epsilon = init_epsilon
success_rates = np.zeros((len(test_states), max_iterations))
for i in range(max_iterations):
data, win_pct, max_ind = self.gather_data(epsilon)
data = (data[0][:max_ind, :],
data[1][:max_ind, :],
data[2][:max_ind, :],
data[3][:max_ind, :])
old_theta = self.theta
for j in range(10):
self.theta = self.train_step(alpha, data)
theta_diff = self.theta - old_theta
diff_max = np.max(np.abs(theta_diff))
theta_max = np.max(np.abs(self.theta))
success_rates[:, i] = self.test_train_iteration(test_states)
avg_rate = np.average(success_rates[:, i])
print("Iter", i, "train_iters", max_ind, "train_win_pct", win_pct, "test_win_pct", avg_rate, "alpha", alpha, "ep", epsilon, "theta_new - theta (max) =", diff_max, "theta_max", theta_max)
epsilon = 0.9 * epsilon
alpha = 0.8 * alpha
if visualise:
self.visualize()
return success_rates
def play(self, init_state=None, render=True, max_iterations=1000):
state = self.reset(init_state).reshape((1,2))
done = False
for i in range(max_iterations):
action = int(self.next_a(state))
next_state, reward, is_done, _ = self.game.step(action)
if render:
self.game.render()
state = np.array(next_state).reshape((1,2))
if is_done:
done = True
break
self.game.close()
return done
def test_train_iteration(self, test_init_states):
results = np.zeros(len(test_init_states))
for state_idx, init_state in enumerate(test_init_states):
result = self.play(init_state, render=False)
results[state_idx] = int(result)
return results
def get_test_states(self, count=10):
return [(np.random.uniform(low=-0.6, high=-0.4), 0) for i in range(count)]
def test_model(self, training_cycles=5, test_states=None, **training_args):
success_rates = None
if test_states is None:
test_states = self.get_test_states()
for t in range(training_cycles):
self.reset_theta()
print("*** TRAINING EXPERIMENT {} ***".format(t))
rates = self.train(test_states=test_states, **training_args)
print("*** RESULT ***")
print(rates)
if success_rates is None:
success_rates = rates
else:
success_rates += rates
success_rates /= training_cycles
return test_states, success_rates
def plot_success_rates(self, success_rates):
plt.figure()
avg = np.average(success_rates, axis=0)
plt.plot(avg)
plt.title('Average success rate per iteration')
plt.xlabel('Iteration')
plt.ylabel('Success rate')
plt.show()
def run_q_learning_training(seed, epsilon=0.1, max_episodes=1000):
env = MountainCarWithResetEnv()
np.random.seed(seed)
env.seed(seed)
gamma = 0.999
learning_rate = 0.01
max_episodes = max_episodes
solver = Solver(
# learning parameters
gamma=gamma,
learning_rate=learning_rate,
# feature extraction parameters
number_of_kernels_per_dim=[7, 5],
# env dependencies (DO NOT CHANGE):
number_of_actions=env.action_space.n,
)
train_statistics = defaultdict(list)
bellman_error = list()
bellman_error_index = 100
for episode_index in range(1, max_episodes + 1):
episode_gain, mean_delta = run_episode(env,
solver,
is_train=True,
epsilon=epsilon)
bellman_error.append(mean_delta)
print(
f'After {episode_index}, reward = {episode_gain}, epsilon {epsilon}, average error {mean_delta}'
)
env.reset()
init_state = env.state
phi_st_0 = solver.get_state_action_features(init_state, 0)
phi_st_1 = solver.get_state_action_features(init_state, 1)
phi_st_2 = solver.get_state_action_features(init_state, 2)
Q_st_0 = phi_st_0.transpose() @ solver.theta
Q_st_1 = phi_st_1.transpose() @ solver.theta
Q_st_2 = phi_st_2.transpose() @ solver.theta
train_statistics["init_state"].append(max(Q_st_0, Q_st_1, Q_st_2))
train_statistics["reward"].append(episode_gain)
if episode_index % 100 == 99:
train_statistics["bellman_error"].append(np.mean(bellman_error))
train_statistics["bellman_error_index"].append(bellman_error_index)
bellman_error_index += 100
bellman_error = list()
if episode_index % 10 == 9:
test_gains = [
run_episode(env, solver, is_train=False, epsilon=0.)[0]
for _ in range(10)
]
mean_test_gain = np.mean(test_gains)
train_statistics["performance"].append(mean_test_gain)
print(f'tested 10 episodes: mean gain is {mean_test_gain}')
if mean_test_gain >= -75.:
print(f'solved in {episode_index} episodes')
break
return train_statistics
def run_lspi(seed,
w_updates=20,
samples_to_collect=100000,
evaluation_number_of_games=1,
evaluation_max_steps_per_game=200,
thresh=0.00001,
only_final=False):
"""
This is the main lspi function
:param seed: random seed for the run
:param w_updates: how many w updates to do
:param samples_to_collect: how many samples to collect
:param evaluation_number_of_games: how many game evaluations to do
:param evaluation_max_steps_per_game: how many steps to allow the evaluation game to run
:param thresh: the threshold for the stopping condition
:param only_final: run evaluation only at the end of the run
:return: None
"""
res_dir = './Results/'
np.random.seed(seed)
number_of_kernels_per_dim = [12, 10]
gamma = 0.999
env = MountainCarWithResetEnv()
# collect data
states, actions, rewards, next_states, done_flags = DataCollector(
env).collect_data(samples_to_collect)
# get data success rate
data_success_rate = np.sum(rewards) / len(rewards)
print('success rate: {}'.format(data_success_rate))
# standardize data
data_transformer = DataTransformer()
data_transformer.set_using_states(
np.concatenate((states, next_states), axis=0))
states = data_transformer.transform_states(states)
next_states = data_transformer.transform_states(next_states)
# process with radial basis functions
feature_extractor = RadialBasisFunctionExtractor(number_of_kernels_per_dim)
# encode all states:
encoded_states = feature_extractor.encode_states_with_radial_basis_functions(
states)
encoded_next_states = feature_extractor.encode_states_with_radial_basis_functions(
next_states)
# set a new linear policy
linear_policy = LinearPolicy(feature_extractor.get_number_of_features(), 3,
True)
# but set the weights as random
linear_policy.set_w(np.random.uniform(size=linear_policy.w.shape))
# start an object that evaluates the success rate over time
evaluator = GamePlayer(env, data_transformer, feature_extractor,
linear_policy)
# success_rate = evaluator.play_games(evaluation_number_of_games, evaluation_max_steps_per_game)
# print("Initial success rate: {}".format(success_rate))
performances = []
if not only_final:
performances.append(
evaluator.play_games(evaluation_number_of_games,
evaluation_max_steps_per_game))
read = False
if read:
with open(res_dir + 'weight.pickle', 'rb') as handle:
new_w = pickle.load(handle)
linear_policy.set_w(np.expand_dims(new_w, 1))
for lspi_iteration in range(w_updates):
print('starting lspi iteration {}'.format(lspi_iteration))
new_w = compute_lspi_iteration(encoded_states, encoded_next_states,
actions, rewards, done_flags,
linear_policy, gamma)
with open(res_dir + 'weight.pickle', 'wb') as handle:
pickle.dump(new_w, handle, protocol=pickle.HIGHEST_PROTOCOL)
norm_diff = linear_policy.set_w(new_w)
if not only_final:
performances.append(
evaluator.play_games(evaluation_number_of_games,
evaluation_max_steps_per_game))
if norm_diff < thresh:
break
print('done lspi')
if not only_final:
with open(res_dir + 'perf' + str(seed) + '.pickle', 'wb') as handle:
pickle.dump(performances, handle, protocol=pickle.HIGHEST_PROTOCOL)
if only_final:
score = evaluator.play_games(evaluation_number_of_games,
evaluation_max_steps_per_game)
with open(res_dir + 'final_perf' + str(samples_to_collect) + '.pickle',
'wb') as handle:
pickle.dump(score, handle, protocol=pickle.HIGHEST_PROTOCOL)
evaluator.play_game(evaluation_max_steps_per_game, render=True)
