def experiment(n_epochs, n_steps, n_steps_test):
np.random.seed()
# MDP
horizon = 1000
gamma = 0.99
gamma_eval = 1.
mdp = Gym('Acrobot-v1', horizon, gamma)
# Policy
epsilon = LinearDecayParameter(value=1., min_value=.01, n=5000)
epsilon_test = Parameter(value=0.)
epsilon_random = Parameter(value=1.)
pi = EpsGreedy(epsilon=epsilon_random)
# Settings
initial_replay_size = 500
max_replay_size = 5000
target_update_frequency = 100
batch_size = 200
n_features = 80
train_frequency = 1
# Approximator
input_shape = mdp.info.observation_space.shape
approximator_params = dict(network=Network,
optimizer={
'class': optim.Adam,
'params': {
'lr': .001
}
},
loss=F.smooth_l1_loss,
n_features=n_features,
input_shape=input_shape,
output_shape=mdp.info.action_space.size,
n_actions=mdp.info.action_space.n)
# Agent
agent = DQN(PyTorchApproximator,
pi,
mdp.info,
approximator_params=approximator_params,
batch_size=batch_size,
n_approximators=1,
initial_replay_size=initial_replay_size,
max_replay_size=max_replay_size,
target_update_frequency=target_update_frequency)
# Algorithm
core = Core(agent, mdp)
core.learn(n_steps=initial_replay_size,
n_steps_per_fit=initial_replay_size)
# RUN
pi.set_epsilon(epsilon_test)
dataset = core.evaluate(n_steps=n_steps_test, render=False)
J = compute_J(dataset, gamma_eval)
print('J: ', np.mean(J))
for n in range(n_epochs):
print('Epoch: ', n)
pi.set_epsilon(epsilon)
core.learn(n_steps=n_steps, n_steps_per_fit=train_frequency)
pi.set_epsilon(epsilon_test)
dataset = core.evaluate(n_steps=n_steps_test, render=False)
J = compute_J(dataset, gamma_eval)
print('J: ', np.mean(J))
print('Press a button to visualize acrobot')
input()
core.evaluate(n_episodes=5, render=True)
def experiment():
np.random.seed()
# Argument parser
parser = argparse.ArgumentParser()
arg_game = parser.add_argument_group('Game')
arg_game.add_argument("--name",
type=str,
default='BreakoutDeterministic-v4',
help='Gym ID of the Atari game.')
arg_game.add_argument("--screen-width", type=int, default=84,
help='Width of the game screen.')
arg_game.add_argument("--screen-height", type=int, default=84,
help='Height of the game screen.')
arg_mem = parser.add_argument_group('Replay Memory')
arg_mem.add_argument("--initial-replay-size", type=int, default=50000,
help='Initial size of the replay memory.')
arg_mem.add_argument("--max-replay-size", type=int, default=500000,
help='Max size of the replay memory.')
arg_net = parser.add_argument_group('Deep Q-Network')
arg_net.add_argument("--optimizer",
choices=['adadelta',
'adam',
'rmsprop',
'rmspropcentered'],
default='adam',
help='Name of the optimizer to use to learn.')
arg_net.add_argument("--learning-rate", type=float, default=.00025,
help='Learning rate value of the optimizer. Only used'
'in rmspropcentered')
arg_net.add_argument("--decay", type=float, default=.95,
help='Discount factor for the history coming from the'
'gradient momentum in rmspropcentered')
arg_net.add_argument("--epsilon", type=float, default=.01,
help='Epsilon term used in rmspropcentered')
arg_alg = parser.add_argument_group('Algorithm')
arg_alg.add_argument("--algorithm", choices=['dqn', 'ddqn', 'adqn'],
default='dqn',
help='Name of the algorithm. dqn is for standard'
'DQN, ddqn is for Double DQN and adqn is for'
'Averaged DQN.')
arg_alg.add_argument("--n-approximators", type=int, default=1,
help="Number of approximators used in the ensemble for"
"Averaged DQN.")
arg_alg.add_argument("--batch-size", type=int, default=32,
help='Batch size for each fit of the network.')
arg_alg.add_argument("--history-length", type=int, default=4,
help='Number of frames composing a state.')
arg_alg.add_argument("--target-update-frequency", type=int, default=10000,
help='Number of learning step before each update of'
'the target network.')
arg_alg.add_argument("--evaluation-frequency", type=int, default=250000,
help='Number of learning step before each evaluation.'
'This number represents an epoch.')
arg_alg.add_argument("--train-frequency", type=int, default=4,
help='Number of learning steps before each fit of the'
'neural network.')
arg_alg.add_argument("--max-steps", type=int, default=50000000,
help='Total number of learning steps.')
arg_alg.add_argument("--final-exploration-frame", type=int, default=1000000,
help='Number of steps until the exploration rate stops'
'decreasing.')
arg_alg.add_argument("--initial-exploration-rate", type=float, default=1.,
help='Initial value of the exploration rate.')
arg_alg.add_argument("--final-exploration-rate", type=float, default=.1,
help='Final value of the exploration rate. When it'
'reaches this values, it stays constant.')
arg_alg.add_argument("--test-exploration-rate", type=float, default=.05,
help='Exploration rate used during evaluation.')
arg_alg.add_argument("--test-samples", type=int, default=125000,
help='Number of steps for each evaluation.')
arg_alg.add_argument("--max-no-op-actions", type=int, default=8,
help='Maximum number of no-op action performed at the'
'beginning of the episodes. The minimum number is'
'history_length. This number is 30 in the DQN'
'Deepmind paper, but they consider the first 30'
'frame without frame skipping.')
arg_alg.add_argument("--no-op-action-value", type=int, default=0,
help='Value of the no-op action.')
arg_utils = parser.add_argument_group('Utils')
arg_utils.add_argument('--load-path', type=str,
help='Path of the model to be loaded.')
arg_utils.add_argument('--save', action='store_true',
help='Flag specifying whether to save the model.')
arg_utils.add_argument('--render', action='store_true',
help='Flag specifying whether to render the game.')
arg_utils.add_argument('--quiet', action='store_true',
help='Flag specifying whether to hide the progress'
'bar.')
arg_utils.add_argument('--debug', action='store_true',
help='Flag specifying whether the script has to be'
'run in debug mode.')
args = parser.parse_args()
scores = list()
# Evaluation of the model provided by the user.
if args.load_path:
# MDP
mdp = Atari(args.name, args.screen_width, args.screen_height,
ends_at_life=False)
# Policy
epsilon_test = Parameter(value=args.test_exploration_rate)
pi = EpsGreedy(epsilon=epsilon_test)
# Approximator
input_shape = (args.screen_height, args.screen_width,
args.history_length)
approximator_params = dict(
input_shape=input_shape,
output_shape=(mdp.info.action_space.n,),
n_actions=mdp.info.action_space.n,
name='test',
load_path=args.load_path,
optimizer={'name': args.optimizer,
'lr': args.learning_rate,
'decay': args.decay,
'epsilon': args.epsilon}
)
approximator = ConvNet
# Agent
algorithm_params = dict(
batch_size=1,
train_frequency=1,
target_update_frequency=1,
initial_replay_size=0,
max_replay_size=0,
history_length=args.history_length,
max_no_op_actions=args.max_no_op_actions,
no_op_action_value=args.no_op_action_value,
dtype=np.uint8
)
agent = DQN(approximator, pi, mdp.info,
approximator_params=approximator_params, **algorithm_params)
# Algorithm
core_test = Core(agent, mdp)
# Evaluate model
pi.set_epsilon(epsilon_test)
dataset = core_test.evaluate(n_steps=args.test_samples,
render=args.render,
quiet=args.quiet)
get_stats(dataset)
else:
# DQN learning run
# Summary folder
folder_name = './logs/atari_' + args.algorithm + '_' + args.name +\
'_' + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
# Settings
if args.debug:
initial_replay_size = 50
max_replay_size = 500
train_frequency = 5
target_update_frequency = 10
test_samples = 20
evaluation_frequency = 50
max_steps = 1000
else:
initial_replay_size = args.initial_replay_size
max_replay_size = args.max_replay_size
train_frequency = args.train_frequency
target_update_frequency = args.target_update_frequency
test_samples = args.test_samples
evaluation_frequency = args.evaluation_frequency
max_steps = args.max_steps
# MDP
mdp = Atari(args.name, args.screen_width, args.screen_height,
ends_at_life=True)
# Policy
epsilon = LinearDecayParameter(value=args.initial_exploration_rate,
min_value=args.final_exploration_rate,
n=args.final_exploration_frame)
epsilon_test = Parameter(value=args.test_exploration_rate)
epsilon_random = Parameter(value=1)
pi = EpsGreedy(epsilon=epsilon_random)
# Approximator
input_shape = (args.screen_height, args.screen_width,
args.history_length)
approximator_params = dict(
input_shape=input_shape,
output_shape=(mdp.info.action_space.n,),
n_actions=mdp.info.action_space.n,
folder_name=folder_name,
optimizer={'name': args.optimizer,
'lr': args.learning_rate,
'decay': args.decay,
'epsilon': args.epsilon}
)
approximator = ConvNet
# Agent
algorithm_params = dict(
batch_size=args.batch_size,
n_approximators=args.n_approximators,
initial_replay_size=initial_replay_size,
max_replay_size=max_replay_size,
history_length=args.history_length,
train_frequency=train_frequency,
target_update_frequency=target_update_frequency,
max_no_op_actions=args.max_no_op_actions,
no_op_action_value=args.no_op_action_value,
dtype=np.uint8
)
if args.algorithm == 'dqn':
agent = DQN(approximator, pi, mdp.info,
approximator_params=approximator_params,
**algorithm_params)
elif args.algorithm == 'ddqn':
agent = DoubleDQN(approximator, pi, mdp.info,
approximator_params=approximator_params,
**algorithm_params)
elif args.algorithm == 'adqn':
agent = AveragedDQN(approximator, pi, mdp.info,
approximator_params=approximator_params,
**algorithm_params)
# Algorithm
core = Core(agent, mdp)
# RUN
# Fill replay memory with random dataset
print_epoch(0)
core.learn(n_steps=initial_replay_size,
n_steps_per_fit=initial_replay_size, quiet=args.quiet)
if args.save:
agent.approximator.model.save()
# Evaluate initial policy
pi.set_epsilon(epsilon_test)
if args.algorithm == 'ddqn':
agent.policy.set_q(agent.target_approximator)
mdp.set_episode_end(False)
dataset = core.evaluate(n_steps=test_samples, render=args.render,
quiet=args.quiet)
scores.append(get_stats(dataset))
if args.algorithm == 'ddqn':
agent.policy.set_q(agent.approximator)
np.save(folder_name + '/scores.npy', scores)
for n_epoch in range(1, max_steps // evaluation_frequency + 1):
print_epoch(n_epoch)
print('- Learning:')
# learning step
pi.set_epsilon(epsilon)
mdp.set_episode_end(True)
core.learn(n_steps=evaluation_frequency,
n_steps_per_fit=train_frequency, quiet=args.quiet)
if args.save:
agent.approximator.model.save()
print('- Evaluation:')
# evaluation step
pi.set_epsilon(epsilon_test)
if args.algorithm == 'ddqn':
agent.policy.set_q(agent.target_approximator)
mdp.set_episode_end(False)
dataset = core.evaluate(n_steps=test_samples, render=args.render,
quiet=args.quiet)
scores.append(get_stats(dataset))
if args.algorithm == 'ddqn':
agent.policy.set_q(agent.approximator)
np.save(folder_name + '/scores.npy', scores)
return scores
optimizer=optimizer,
loss=F.smooth_l1_loss)
approximator = TorchApproximator
# Agent
algorithm_params = dict(batch_size=32,
target_update_frequency=target_update_frequency //
train_frequency,
replay_memory=None,
initial_replay_size=initial_replay_size,
max_replay_size=max_replay_size)
agent = DQN(approximator,
pi,
mdp.info,
approximator_params=approximator_params,
**algorithm_params)
# Algorithm
core = Core(agent, mdp)
# RUN
# Fill replay memory with random dataset
print_epoch(0)
core.learn(n_steps=initial_replay_size, n_steps_per_fit=initial_replay_size)
# Evaluate initial policy
pi.set_epsilon(epsilon_test)
mdp.set_episode_end(False)
def experiment(alg):
gym.logger.setLevel(0)
np.random.seed(88)
tf.set_random_seed(88)
# DQN settings
initial_replay_size = 500
max_replay_size = 1000
train_frequency = 50
target_update_frequency = 100
evaluation_frequency = 200
max_steps = 2000
# MDP train
mdp = Atari('BreakoutDeterministic-v4', 84, 84, ends_at_life=True)
# Policy
epsilon = LinearDecayParameter(value=1, min_value=.1, n=10)
epsilon_test = Parameter(value=.05)
epsilon_random = Parameter(value=1)
pi = EpsGreedy(epsilon=epsilon_random)
# Approximator
input_shape = (84, 84, 4)
approximator_params = dict(
input_shape=input_shape,
output_shape=(mdp.info.action_space.n, ),
n_actions=mdp.info.action_space.n,
input_preprocessor=[Scaler(mdp.info.observation_space.high[0, 0])],
optimizer={
'name': 'rmsprop',
'lr': .00025,
'decay': .95,
'epsilon': 1e-10
})
approximator = ConvNet
# Agent
algorithm_params = dict(batch_size=32,
initial_replay_size=initial_replay_size,
n_approximators=2 if alg == 'adqn' else 1,
max_replay_size=max_replay_size,
history_length=4,
train_frequency=train_frequency,
target_update_frequency=target_update_frequency,
max_no_op_actions=10,
no_op_action_value=0)
fit_params = dict()
agent_params = {
'approximator_params': approximator_params,
'algorithm_params': algorithm_params,
'fit_params': fit_params
}
if alg == 'dqn':
agent = DQN(approximator, pi, mdp.info, agent_params)
elif alg == 'ddqn':
agent = DoubleDQN(approximator, pi, mdp.info, agent_params)
elif alg == 'adqn':
agent = AveragedDQN(approximator, pi, mdp.info, agent_params)
# Algorithm
core = Core(agent, mdp)
# DQN
# fill replay memory with random dataset
core.learn(n_steps=initial_replay_size,
n_steps_per_fit=initial_replay_size,
quiet=True)
# evaluate initial policy
pi.set_epsilon(epsilon_test)
mdp.set_episode_end(ends_at_life=False)
for n_epoch in xrange(1, max_steps / evaluation_frequency + 1):
# learning step
pi.set_epsilon(epsilon)
mdp.set_episode_end(ends_at_life=True)
core.learn(n_steps=evaluation_frequency,
n_steps_per_fit=train_frequency,
quiet=True)
# evaluation step
pi.set_epsilon(epsilon_test)
mdp.set_episode_end(ends_at_life=False)
w = agent.approximator.model.get_weights(only_trainable=True)
return w
def experiment():
np.random.seed()
# Argument parser
parser = argparse.ArgumentParser()
arg_game = parser.add_argument_group('Game')
arg_game.add_argument("--name",
type=str,
default='BreakoutDeterministic-v4',
help='Gym ID of the Atari game.')
arg_game.add_argument("--screen-width", type=int, default=84,
help='Width of the game screen.')
arg_game.add_argument("--screen-height", type=int, default=84,
help='Height of the game screen.')
arg_mem = parser.add_argument_group('Replay Memory')
arg_mem.add_argument("--initial-replay-size", type=int, default=50000,
help='Initial size of the replay memory.')
arg_mem.add_argument("--max-replay-size", type=int, default=500000,
help='Max size of the replay memory.')
arg_mem.add_argument("--prioritized", action='store_true',
help='Whether to use prioritized memory or not.')
arg_net = parser.add_argument_group('Deep Q-Network')
arg_net.add_argument("--optimizer",
choices=['adadelta',
'adam',
'rmsprop',
'rmspropcentered'],
default='rmsprop',
help='Name of the optimizer to use.')
arg_net.add_argument("--learning-rate", type=float, default=.00025,
help='Learning rate value of the optimizer.')
arg_net.add_argument("--decay", type=float, default=.95,
help='Discount factor for the history coming from the'
'gradient momentum in rmspropcentered and'
'rmsprop')
arg_net.add_argument("--epsilon", type=float, default=1e-8,
help='Epsilon term used in rmspropcentered and'
'rmsprop')
arg_alg = parser.add_argument_group('Algorithm')
arg_alg.add_argument("--algorithm", choices=['dqn', 'ddqn', 'adqn', 'cdqn'],
default='dqn',
help='Name of the algorithm. dqn is for standard'
'DQN, ddqn is for Double DQN and adqn is for'
'Averaged DQN.')
arg_alg.add_argument("--n-approximators", type=int, default=1,
help="Number of approximators used in the ensemble for"
"Averaged DQN.")
arg_alg.add_argument("--batch-size", type=int, default=32,
help='Batch size for each fit of the network.')
arg_alg.add_argument("--history-length", type=int, default=4,
help='Number of frames composing a state.')
arg_alg.add_argument("--target-update-frequency", type=int, default=10000,
help='Number of collected samples before each update'
'of the target network.')
arg_alg.add_argument("--evaluation-frequency", type=int, default=250000,
help='Number of collected samples before each'
'evaluation. An epoch ends after this number of'
'steps')
arg_alg.add_argument("--train-frequency", type=int, default=4,
help='Number of collected samples before each fit of'
'the neural network.')
arg_alg.add_argument("--max-steps", type=int, default=50000000,
help='Total number of collected samples.')
arg_alg.add_argument("--final-exploration-frame", type=int, default=1000000,
help='Number of collected samples until the exploration'
'rate stops decreasing.')
arg_alg.add_argument("--initial-exploration-rate", type=float, default=1.,
help='Initial value of the exploration rate.')
arg_alg.add_argument("--final-exploration-rate", type=float, default=.1,
help='Final value of the exploration rate. When it'
'reaches this values, it stays constant.')
arg_alg.add_argument("--test-exploration-rate", type=float, default=.05,
help='Exploration rate used during evaluation.')
arg_alg.add_argument("--test-samples", type=int, default=125000,
help='Number of collected samples for each'
'evaluation.')
arg_alg.add_argument("--max-no-op-actions", type=int, default=30,
help='Maximum number of no-op actions performed at the'
'beginning of the episodes.')
arg_alg.add_argument("--n-atoms", type=int, default=51,
help='Number of atoms for Categorical DQN.')
arg_alg.add_argument("--v-min", type=int, default=-10,
help='Minimum action-value for Categorical DQN.')
arg_alg.add_argument("--v-max", type=int, default=10,
help='Maximum action-value for Categorical DQN.')
arg_utils = parser.add_argument_group('Utils')
arg_utils.add_argument('--use-cuda', action='store_true',
help='Flag specifying whether to use the GPU.')
arg_utils.add_argument('--load-path', type=str,
help='Path of the model to be loaded.')
arg_utils.add_argument('--save', action='store_true',
help='Flag specifying whether to save the model.')
arg_utils.add_argument('--render', action='store_true',
help='Flag specifying whether to render the game.')
arg_utils.add_argument('--quiet', action='store_true',
help='Flag specifying whether to hide the progress'
'bar.')
arg_utils.add_argument('--debug', action='store_true',
help='Flag specifying whether the script has to be'
'run in debug mode.')
args = parser.parse_args()
scores = list()
optimizer = dict()
if args.optimizer == 'adam':
optimizer['class'] = optim.Adam
optimizer['params'] = dict(lr=args.learning_rate,
eps=args.epsilon)
elif args.optimizer == 'adadelta':
optimizer['class'] = optim.Adadelta
optimizer['params'] = dict(lr=args.learning_rate,
eps=args.epsilon)
elif args.optimizer == 'rmsprop':
optimizer['class'] = optim.RMSprop
optimizer['params'] = dict(lr=args.learning_rate,
alpha=args.decay,
eps=args.epsilon)
elif args.optimizer == 'rmspropcentered':
optimizer['class'] = optim.RMSprop
optimizer['params'] = dict(lr=args.learning_rate,
alpha=args.decay,
eps=args.epsilon,
centered=True)
else:
raise ValueError
# Evaluation of the model provided by the user.
if args.load_path:
# MDP
mdp = Atari(args.name, args.screen_width, args.screen_height,
ends_at_life=False, history_length=args.history_length,
max_no_op_actions=args.max_no_op_actions)
# Policy
epsilon_test = Parameter(value=args.test_exploration_rate)
pi = EpsGreedy(epsilon=epsilon_test)
# Approximator
input_shape = (args.history_length, args.screen_height,
args.screen_width)
approximator_params = dict(
network=Network,
input_shape=input_shape,
output_shape=(mdp.info.action_space.n,),
n_actions=mdp.info.action_space.n,
load_path=args.load_path,
optimizer=optimizer,
loss=F.smooth_l1_loss,
use_cuda=args.use_cuda
)
approximator = TorchApproximator
# Agent
algorithm_params = dict(
batch_size=1,
train_frequency=1,
target_update_frequency=1,
initial_replay_size=0,
max_replay_size=0
)
agent = DQN(approximator, pi, mdp.info,
approximator_params=approximator_params, **algorithm_params)
# Algorithm
core_test = Core(agent, mdp)
# Evaluate model
pi.set_epsilon(epsilon_test)
dataset = core_test.evaluate(n_steps=args.test_samples,
render=args.render,
quiet=args.quiet)
get_stats(dataset)
else:
# DQN learning run
# Summary folder
folder_name = './logs/atari_' + args.algorithm + '_' + args.name +\
'_' + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
pathlib.Path(folder_name).mkdir(parents=True)
# Settings
if args.debug:
initial_replay_size = 50
max_replay_size = 500
train_frequency = 5
target_update_frequency = 10
test_samples = 20
evaluation_frequency = 50
max_steps = 1000
else:
initial_replay_size = args.initial_replay_size
max_replay_size = args.max_replay_size
train_frequency = args.train_frequency
target_update_frequency = args.target_update_frequency
test_samples = args.test_samples
evaluation_frequency = args.evaluation_frequency
max_steps = args.max_steps
# MDP
mdp = Atari(args.name, args.screen_width, args.screen_height,
ends_at_life=True, history_length=args.history_length,
max_no_op_actions=args.max_no_op_actions)
# Policy
epsilon = LinearParameter(value=args.initial_exploration_rate,
threshold_value=args.final_exploration_rate,
n=args.final_exploration_frame)
epsilon_test = Parameter(value=args.test_exploration_rate)
epsilon_random = Parameter(value=1)
pi = EpsGreedy(epsilon=epsilon_random)
class CategoricalLoss(nn.Module):
def forward(self, input, target):
input = input.clamp(1e-5)
return -torch.sum(target * torch.log(input))
# Approximator
input_shape = (args.history_length, args.screen_height,
args.screen_width)
approximator_params = dict(
network=Network if args.algorithm != 'cdqn' else FeatureNetwork,
input_shape=input_shape,
output_shape=(mdp.info.action_space.n,),
n_actions=mdp.info.action_space.n,
n_features=Network.n_features,
optimizer=optimizer,
loss=F.smooth_l1_loss if args.algorithm != 'cdqn' else CategoricalLoss(),
use_cuda=args.use_cuda
)
approximator = TorchApproximator
if args.prioritized:
replay_memory = PrioritizedReplayMemory(
initial_replay_size, max_replay_size, alpha=.6,
beta=LinearParameter(.4, threshold_value=1,
n=max_steps // train_frequency)
)
else:
replay_memory = None
# Agent
algorithm_params = dict(
batch_size=args.batch_size,
n_approximators=args.n_approximators,
target_update_frequency=target_update_frequency // train_frequency,
replay_memory=replay_memory,
initial_replay_size=initial_replay_size,
max_replay_size=max_replay_size
)
if args.algorithm == 'dqn':
agent = DQN(approximator, pi, mdp.info,
approximator_params=approximator_params,
**algorithm_params)
elif args.algorithm == 'ddqn':
agent = DoubleDQN(approximator, pi, mdp.info,
approximator_params=approximator_params,
**algorithm_params)
elif args.algorithm == 'adqn':
agent = AveragedDQN(approximator, pi, mdp.info,
approximator_params=approximator_params,
**algorithm_params)
elif args.algorithm == 'cdqn':
agent = CategoricalDQN(pi, mdp.info,
approximator_params=approximator_params,
n_atoms=args.n_atoms, v_min=args.v_min,
v_max=args.v_max, **algorithm_params)
# Algorithm
core = Core(agent, mdp)
# RUN
# Fill replay memory with random dataset
print_epoch(0)
core.learn(n_steps=initial_replay_size,
n_steps_per_fit=initial_replay_size, quiet=args.quiet)
if args.save:
np.save(folder_name + '/weights-exp-0-0.npy',
agent.approximator.get_weights())
# Evaluate initial policy
pi.set_epsilon(epsilon_test)
mdp.set_episode_end(False)
dataset = core.evaluate(n_steps=test_samples, render=args.render,
quiet=args.quiet)
scores.append(get_stats(dataset))
np.save(folder_name + '/scores.npy', scores)
for n_epoch in range(1, max_steps // evaluation_frequency + 1):
print_epoch(n_epoch)
print('- Learning:')
# learning step
pi.set_epsilon(epsilon)
mdp.set_episode_end(True)
core.learn(n_steps=evaluation_frequency,
n_steps_per_fit=train_frequency, quiet=args.quiet)
if args.save:
np.save(folder_name + '/weights-exp-0-' + str(n_epoch) + '.npy',
agent.approximator.get_weights())
print('- Evaluation:')
# evaluation step
pi.set_epsilon(epsilon_test)
mdp.set_episode_end(False)
dataset = core.evaluate(n_steps=test_samples, render=args.render,
quiet=args.quiet)
scores.append(get_stats(dataset))
np.save(folder_name + '/scores.npy', scores)
return scores
