def act(self, state, available=None, step=0):
"""
select an action in the provided state
:param state: state to select action in, must be provided in non-linearized, permutation form
:param available: vector containing the actions that lead to non-zero reward in this step in one-hot encoding
:param step: count how many episodes have been performed to evaluate the epsilon-greedy look-ahead
:return: action to perform in the specific state
"""
# Compute the value for epsilon depending on the step
if step < self.steps_epsilon:
epsilon = 1 - (1 - self.epsilon_end) * (step / self.steps_epsilon)
else:
epsilon = self.epsilon_end
# depending on the epsilon value, select an action ...
if self.look_ahead_search and epsilon > 0 and random.uniform(
0, 1) < epsilon:
# ... randomly or ...
action = self.look_ahead()
value, probs = self.net.forward(
linearize_state(state, self.num_seqs))
log_prob = torch.log(probs[action])
else:
# ... depending on the network state
action, value, log_prob = self.training_agent.act(
linearize_state(state, self.num_seqs),
available if self.supported_search else None)
return action, value, log_prob
def train(self, print_progress):
"""
Performs the learning process.
:return: the returns, losses and invalid action ratios computed during the training process
(usable for analytical and optimization tasks)
"""
episode_reward, episode_loss, episode_fails = 0, (0, 0), 0
avg_rewards, avg_losses, avg_fails = [], [], []
# play as many games as specified for the training
for step in range(self.games):
# print the progress the model made while learning
if (step +
1) % self.plot_size == 0 or self.env.align_table.is_full():
tmp_reward, tmp_loss, tmp_fail = self.print_progress(
print_progress, step, episode_reward, episode_loss,
episode_fails)
avg_rewards.append(tmp_reward)
avg_losses.append(tmp_loss)
avg_fails.append(tmp_fail)
episode_reward, episode_loss, episode_fails = 0, (0, 0), 0
# if all alignments have been found exit
if self.env.align_table.is_full():
if self.env.best_alignment == (Profile([]), None):
self.env.best_alignment = self.env.align_table.get_best(
self.score)
if print_progress:
print(
"Search exited. All alignments have been visited and optimality is guaranteed."
)
break
game_reward, state, _, done = self.env.soft_reset()
# play new game
tmp_erb = []
while not done:
# compute the action, perform it in the environment and add all stats to the local replay-buffer
action, value, log_prob = self.act(state, self.env.available)
prev_state = np.array(state)
game_reward, state, _, done = self.env.step(action)
tmp_erb.append(
(linearize_state(prev_state, self.num_seqs), action,
game_reward[self.score],
linearize_state(state,
self.num_seqs), done, value, log_prob))
# update reward according to the received reward
episode_reward += game_reward[self.score]
if not self.refinement and len(state) != self.num_seqs:
episode_fails += 1
# learn from the played game
episode_loss = [
e + l for e, l in zip(episode_loss, self.learn(tmp_erb))
]
return avg_rewards, avg_losses, avg_fails
def get_state_estimate(self, state):
"""
get estimate of actual state based on previous learning
:param state: actual state to estimate
:return: state estimate
"""
return self.net.forward(linearize_state(state, self.num_seqs))[0]
def get_state_estimate(self, state):
"""
Estimate the state based on the actual network-state using its state-estimation output-neuron
:param state: state to estimate
:return: estimation of the expected reward form that state on
"""
return self.net.forward(linearize_state(state, self.num_seqs))[0]
def act(self, state, available=None, step=0):
"""
Selects an action based on the given input-state.
:param state: state to select action for, must be provided in non-linearized, permutation form
:param available: vector containing the actions that lead to non-zero reward in this step in one-hot encoding
:param step: step in which this action is made, needed for epsilon-greediness
:returns: the selected action
"""
# Compute the value for epsilon depending on the step
if step < self.steps_epsilon:
epsilon = 1 - (1 - self.epsilon_end) * (step / self.steps_epsilon)
else:
epsilon = self.epsilon_end
# depending on the epsilon value, select an action...
if epsilon > 0 and random.uniform(0, 1) < epsilon:
if self.look_ahead_search and random.uniform(0, 1) < 0.5:
action = self.look_ahead()
else:
# ...randomly
action = random.choice(
list(set(range(self.num_seqs)) -
set(state))) - (1 if self.refinement else 0)
else:
# ...depending on the network state
action = self.training_agent.act(
linearize_state(state, self.num_seqs),
available if self.supported_search else None)
return action
def select(self, state):
"""
Override the select-method from the super-class (needed for final alignment)
:param state: state of alignment in non-linearized permutation form
:return: action to select in the actual state
"""
return self.act(linearize_state(state, self.num_seqs))
def select(self, state):
"""
select the next action according to the actual network state
needed to override the parent method
:param state: state to select next sequence from in non-linearized form (just as permutation)
:return: selected action as index of next sequence
"""
return self.act(linearize_state(state, self.num_seqs))
def select(self, state):
"""
Select an action based on the input state
:param state: non-linearized state to select the action for
:return: action selected based on the probabilities got from the net
"""
probs = self.net.forward(linearize_state(state, self.num_seqs))[1].detach().numpy()
action = np.random.choice(range(self.num_seqs), p=probs)
if self.refinement:
action -= 1
return action
def select(self, state):
"""
select the next action according to the actual network state
needed to override the parent method
:param state: state to select next sequence from in non-linearized form (just as permutation)
:return: selected action as index of next sequence
"""
action = self.net.forward(linearize_state(
state, self.num_seqs))[1].argmax().item()
if self.refinement:
action -= 1
return action
def generate_mcts_episode(self):
"""
Generate training data based on which actions the network would perform and
what an MCTS-Supervisor thinks about the resulting states
:return:
"""
replay_buffer = []
_, state, _, done = self.env.soft_reset()
while not done:
# evaluate the state using mcts to get move-probabilities and a state estimate
probs, s_est = self.mcts_generator.get_probabilities(state)
# use the networks actual state to select the next action
with torch.no_grad():
action = self.training_agent.select(state)
# append the data to the replay-buffer
replay_buffer.append((linearize_state(state, self.num_seqs), probs, s_est))
# and apply the selected action to the state
_, state, _, done = self.env.step(action)
return replay_buffer