def perform_validation_step(self, task_index):
"""
Perform validation steps for the task from index task_index.
Args:
task_index: task index
Returns:
(rewards, traces lengths)
"""
validation_rewards = []
traces_lengths = []
for _ in range(self.num_validation_episodes):
# Start new episode
mcts = MCTS(self.policy, self.env, task_index,
**self.mcts_test_params)
# Sample an execution trace with mcts using policy as a prior
trace = mcts.sample_execution_trace()
task_reward, trace_length, progs_failed_indices = trace[7], len(
trace[3]), trace[10]
validation_rewards.append(task_reward)
traces_lengths.append(trace_length)
return validation_rewards, traces_lengths, progs_failed_indices
def play_iteration(self, task_index, verbose=False):
"""
Play one training iteration, i.e. select a task, play episodes, store experience in buffer and sample batches
to perform gradient descent on policy weights.
"""
# Get new task to attempt
task_name = self.env.get_program_from_index(task_index)
if self.verbose:
print('Attempt task {} (length {})for {} episodes'.format(
task_name, self.env.length, self.num_episodes_per_task))
# Start training on the task
for episode in range(self.num_episodes_per_task):
if self.verbose:
print('=> Episode: %d' % (episode))
# Start new episode
mcts = MCTS(self.policy, self.env, task_index,
**self.mcts_train_params)
# Sample an execution trace with mcts using policy as a prior
res = mcts.sample_execution_trace()
observations, prog_indices, previous_actions_indices, policy_labels, lstm_states, _, _, \
task_reward, clean_sub_execution, rewards, programs_failed_indices, \
programs_failed_initstates = res
if self.verbose:
print("Task_reward:")
print(task_reward)
print("Rewards:")
print(rewards)
# record trace and store it in buffer only if no problem in sub-programs execution
if clean_sub_execution:
# Generates trace
trace = list(
zip(observations, prog_indices, lstm_states, policy_labels,
rewards))
# Append trace to buffer
self.buffer.append_trace(trace)
else:
if self.verbose:
print("Trace has not been stored in buffer.")
# Decrease statistics of programs that failed
#for idx in programs_failed_indices:
#self.curriculum_scheduler.update_statistics(idx, torch.FloatTensor([0.0]))
# Train policy on batch
if self.buffer.get_memory_length() > self.batch_size:
for _ in range(self.num_updates_per_episode):
batch = self.buffer.sample_batch(self.batch_size)
if batch is not None:
self.policy.train_on_batch(batch)
if verbose:
print("Done episode {}/{}".format(episode + 1,
self.num_episodes_per_task))
'max_depth_dict': max_depth_dict,
'temperature': conf.temperature,
'c_puct': conf.c_puct,
'exploit': True,
'level_closeness_coeff': conf.level_closeness_coeff,
'gamma': conf.gamma
}
for _ in range(40):
env = ListEnv(length=len, encoding_dim=conf.encoding_dim)
bubblesort_index = env.programs_library['BUBBLESORT']['index']
# Test with mcts
mcts = MCTS(policy, env, bubblesort_index, **mcts_test_params)
res = mcts.sample_execution_trace()
mcts_reward = res[7]
mcts_rewards.append(mcts_reward)
if mcts_reward > 0:
mcts_rewards_normalized.append(1.0)
else:
mcts_rewards_normalized.append(0.0)
# Test with network alone
network_only = NetworkOnly(policy, env, max_depth_dict)
netonly_reward, _ = network_only.play(bubblesort_index)
network_only_rewards.append(netonly_reward)
mcts_rewards_normalized_mean = np.mean(
np.array(mcts_rewards_normalized))
mcts_rewards_mean = np.mean(np.array(mcts_rewards))
