class SMDPPlanning:
""" Estimates value function given learned models R and P """
def __init__(self,
env: MiniGridEnv,
R: np.ndarray,
P: np.ndarray,
loglevel: int = 10):
self.env = env
option_dim, self.state_space_dim = R.shape[0], R.shape[1:]
state_space_flat = np.prod(self.state_space_dim)
self.R = R.reshape((option_dim, state_space_flat))
self.P = P.reshape((option_dim, state_space_flat, state_space_flat))
self.V = np.zeros(state_space_flat)
self.logger = ProjectLogger(level=loglevel, printing=True)
def svi(self, θ: float = 1e-9):
""" Iterative Policy Evaluation using Synchronous Value Iteration.
Estimates V by acting greedy wrt to V_hat (current estimate of V)
"""
δ = float('inf')
while δ > θ:
v_old = self.V
self.V = (self.R + np.dot(self.P, self.V)).max(axis=0)
δ = np.sum(np.abs(self.V - v_old))
self.logger.debug(f'State-value delta: {δ}')
yield self.V.reshape(self.state_space_dim)
return self.V.reshape(self.state_space_dim)
def __init__(self,
env: MiniGridEnv,
options: List[Option],
target_policy: PolicyOverOptions,
feature_generator: FeatureGenerator,
weights, # TODO: type?
lr: LearningRate = LearningRate(1, 1, 0),
gamma: float = 0.9,
loglevel: int = 20,
):
self.env = env
# TODO: record all the rewards at the SMDP level?
self.cumulant = lambda: 0
super().__init__(target_policy=target_policy,
termination=lambda: 1,
eligibility=lambda: 1,
cumulant=self.cumulant,
feature=feature_generator,
behavioural_policy=target_policy,
weights=weights,
id=repr(self))
self.options = self.Ω = options
self.option_idx_dict = {str(o): i for i, o in enumerate(self.options)}
self.lr = lr
self.gamma = gamma
self.logger = ProjectLogger(level=loglevel, printing=False)
def __init__(self, env: MiniGridEnv, options: Options, loglevel: int = 10):
self.env = env
self.options = options
self.option_names_dict = {o.name: o for o in self.options}
self.option_idx_dict = {
name: i
for i, name in enumerate(self.option_names_dict)
}
self.logger = ProjectLogger(level=loglevel, printing=True)
def __init__(self,
env: MiniGridEnv,
R: np.ndarray,
P: np.ndarray,
loglevel: int = 10):
self.env = env
option_dim, self.state_space_dim = R.shape[0], R.shape[1:]
state_space_flat = np.prod(self.state_space_dim)
self.R = R.reshape((option_dim, state_space_flat))
self.P = P.reshape((option_dim, state_space_flat, state_space_flat))
self.V = np.zeros(state_space_flat)
self.logger = ProjectLogger(level=loglevel, printing=True)
def __init__(self,
env: MiniGridEnv,
critic: IntraOptionQLearning,
actor: PolicyOverOptions,
action_critic: IntraOptionActionLearning = None,
gamma: float = 0.99,
loglevel: int = 20):
self.env = env
self.γ = gamma
self.critic = critic
self.actor = actor
self.logger = ProjectLogger(level=loglevel)
if action_critic is not None:
self.advantage_estimator = None
self.action_critic = action_critic
else:
self.advantage_estimator = 'io'
self.logger.info(f'Action-critic was not provided, '
f'so the advantages for PG would be estimated')
def __init__(self,
options: List[Option],
rng: np.random.RandomState,
loglevel: int):
self.options = options
self.option_names_dict = {str(o): o for o in self.options}
self.option_idx_dict = {name: i for i, name in
enumerate(self.option_names_dict)}
self.rng = rng
self.logger = ProjectLogger(level=loglevel, printing=False)
def __init__(
self,
env: MiniGridEnv,
feature_generator: NatureConvBody,
critic: IntraOptionDeepQLearning,
actor: PolicyOverOptions,
optimizer: torch.optim.Optimizer,
gamma: float = 0.99,
loglevel: int = 20,
rng: np.random.RandomState = np.random.RandomState(1),
):
self.env = env
self.γ = gamma
# shared between both critic and actor
self.feature_generator = feature_generator
self.critic = critic
self.actor = actor
# self.network = torch.nn.Sequential(self.feature_generator, self.critic.critic)
self.target_network = deepcopy(self.critic.critic)
self.optimizer = optimizer
self.logger = ProjectLogger(level=loglevel)
self.rng = rng
class SMDPValueLearning:
""" Algorithms for finding an optimal policy over a set of options in SMDP.
Treats each option as an indivisible unit.
Does not work well in this setting, since the rooms are larger than in the original experiment
and thus the probability of stumbling across a goal state, while performing primitive actions only is much smaller.
Consider a case when the agent is at the hallway state. It can try to make a primitive action
in the direction of the goal. However, at the next state it can choose to take an option that takes it
either to the other hallway or back with probability 2/5. As the agent makes progress towards the goal state,
it's more likely that the option will get activated along the way. Since no intra-option learning is happening,
the value is not attributed to the states surrounding the goal.
"""
def __init__(self,
env: MiniGridEnv,
options: Options,
policy: PolicyOverOptions,
loglevel: int = 20):
self.env = env
self.options = options
self.option_names_dict = {o.name: o for o in self.options}
self.option_idx_dict = {
name: i
for i, name in enumerate(self.option_names_dict)
}
self._policy = policy
self.logger = ProjectLogger(level=loglevel, printing=False)
def policy(self, state, *args, **kwargs):
option = self._policy(state, *args, **kwargs)
return MarkovOption(starting_state=state,
initiation_set=option.initiation_set,
termination_function=option.termination,
policy=option.target_policy,
name=str(option))
def q_learning(self,
n_episodes: int,
γ: float = 0.9,
Q: np.ndarray = None,
N: np.ndarray = None,
α: float = None,
render: bool = False):
env = self.env.unwrapped
n_options = len(self.options)
state_space_dim = (4, env.width, env.height)
dim = (n_options, *state_space_dim)
if Q is None:
N = np.zeros(dim)
Q = np.zeros(dim)
for episode in range(n_episodes):
self.env.reset()
state = (env.agent_dir, *reversed(env.agent_pos))
executing_option = self.policy(Q, state)
done = False
while not done:
# Step through environment
a = executing_option.policy(state)
obs, reward, done, info = self.env.step(a)
# TODO: infer the state of the agent from obs, i.e. make it POMDP
s_next = (env.agent_dir, *reversed(env.agent_pos))
if render:
action_name = list(env.actions)[a].name
self.logger.debug(f"State: {state}, "
f"Option: {executing_option}, "
f"Action: {action_name}, "
f"Next State: {s_next}")
self.env.render()
time.sleep(0.05)
# Update option
executing_option.k += 1
executing_option.cumulant += γ**executing_option.k * reward
# Check for termination condition and update action-values
if executing_option.termination_function(s_next) == 1 or done:
start_state = (self.option_idx_dict[executing_option.name],
*executing_option.starting_state)
# Determine the step-size
if α is None:
N[start_state] += 1
alpha = 1 / N[start_state]
else:
alpha = α
# Update Q in the direction of the optimal action
r = executing_option.cumulant
k = executing_option.k
o = randargmax(Q[(slice(None), *s_next)])
target = r + γ**k * Q[(o, *s_next)]
Q[start_state] += alpha * (target - Q[start_state])
# Choose the next option
executing_option = self.policy(Q, s_next)
# Reset the state
state = s_next
yield Q, self.env.step_count
return Q, self.env.step_count
@staticmethod
def plot_episode_duration(steps_per_episode):
traces = list()
for option_set, steps_per_episode in steps_per_episode.items():
traces.append(
go.Scatter(
mode='lines',
y=steps_per_episode,
name=option_set,
))
layout = dict(height=700,
showlegend=True,
xaxis=dict(title='Episodes', ),
yaxis=dict(title='Steps per episode', ))
return {'data': traces, 'layout': layout}
class SMDPModelLearning:
""" Model learning in Semi-Markov Decision Process via MC sampling """
def __init__(self, env: MiniGridEnv, options: Options, loglevel: int = 10):
self.env = env
self.options = options
self.option_names_dict = {o.name: o for o in self.options}
self.option_idx_dict = {
name: i
for i, name in enumerate(self.option_names_dict)
}
self.logger = ProjectLogger(level=loglevel, printing=True)
def __str__(self):
return 'SMDPModelLearning'
def choose_option(self, state):
""" Picks an option at random """
options = [o for o in self.options if o.initiation_set[state] == 1]
option = random.choice(options)
return MarkovOption(starting_state=state,
initiation_set=option.initiation_set,
termination_function=option.termination_function,
policy=option.policy,
name=str(option))
def run_episode(self,
N: np.ndarray = None,
R: np.ndarray = None,
P: np.ndarray = None,
γ: float = 0.9,
render: bool = False):
env = self.env.unwrapped
n_options = len(self.options)
state_space_dim = (4, env.width, env.height)
dim = (n_options, *state_space_dim)
if R is None:
R = np.zeros(dim)
N = np.zeros(dim)
P = np.zeros((n_options, *state_space_dim, *state_space_dim))
self.env.reset()
state = (self.env.agent_dir, *reversed(env.agent_pos))
done = False
executing_option = None
while not done:
if executing_option is None:
executing_option = self.choose_option(state)
a = executing_option.policy(state)
obs, reward, done, info = self.env.step(a)
state_next = (env.agent_dir, *reversed(env.agent_pos))
if render:
action_name = list(env.actions)[a].name
self.logger.debug(f"State: {state}, "
f"Option: {executing_option}, "
f"Action: {action_name}, "
f"Next State: {state_next}")
self.env.render()
time.sleep(0.05)
executing_option.k += 1
executing_option.cumulant += γ**executing_option.k * reward
# Check for termination condition and update the model
if executing_option.termination_function(state_next) == 1:
option_state = (self.option_idx_dict[executing_option.name],
*executing_option.starting_state)
# Update visitation counter
N[option_state] += 1
α = 1 / N[option_state]
# Update reward matrix
R[option_state] += α * (executing_option.cumulant -
R[option_state])
# Update probability transition matrix
P[(*option_state, *state_next)] += α * (γ**executing_option.k)
P[option_state] -= α * P[option_state]
executing_option = None
state = state_next
yield N, R, P
return N, R, P
def get_true_models(self, seed: int = 1337, γ: float = 0.9):
""" Learn true dynamics (P) and reward (R) models by unrolling each
option for each state in its initiation set until termination
# TODO: need to call multiple times if the environment is stochastic?
"""
np.random.seed(seed)
n_options = len(self.options)
state_space_dim = (4, self.env.width, self.env.height)
dim = (n_options, *state_space_dim)
R = np.zeros(dim)
N = np.zeros(dim)
P = np.zeros((n_options, *state_space_dim, *state_space_dim))
self.env.reset()
for option_i, option in tqdm(enumerate(self.options)):
for state, active in np.ndenumerate(option.initiation_set):
# Checks if the state is in initiation set
if not active:
continue
env = self.env.unwrapped
env.agent_dir, env.agent_pos = state[0], tuple(
reversed(state[1:]))
cell = self.env.grid.get(*env.agent_pos)
# Check if the state is valid for the agent to be in
if not (cell is None or cell.can_overlap()):
continue
# Activate an option and run until termination
option = MarkovOption(starting_state=state,
initiation_set=option._initiation_set,
termination_set=option._termination_set,
policy=option._policy,
name=str(option))
while True:
a = option.policy(state)
obs, reward, done, info = self.env.step(a)
env = self.env.unwrapped
state_next = (env.agent_dir, *reversed(env.agent_pos))
self.logger.debug(f"State: {state}, "
f"Option: {option}, "
f"Action: {a}, "
f"Next State: {state_next}")
state = state_next
option.k += 1
option.cumulant += γ**option.k * reward
if option.termination_function(state):
break
# Update option models
option_state = (option_i, *option.starting_state)
R[option_state] = option.cumulant
P[(*option_state, *state)] = γ**option.k
return N, R, P
class IntraOptionQLearning(ControlDemon):
"""
We turn now to the intra-option learning of option values and thus of optimal policies
over options. If the options are semi-Markov, then again the SMDP methods described in
Section 5.2 are probably the only feasible methods; a semi-Markov option must be completed
before it can be evaluated in any way. But if the options are Markov and we are willing to
look inside them, then we can consider intra-option methods. Just as in the case of model
learning, intra-option methods for value learning are potentially more efficient than SMDP
methods because they extract more training examples from the same experience.
"""
def __init__(self,
env: MiniGridEnv,
options: List[Option],
target_policy: PolicyOverOptions,
feature_generator: FeatureGenerator,
weights, # TODO: type?
lr: LearningRate = LearningRate(1, 1, 0),
gamma: float = 0.9,
loglevel: int = 20,
):
self.env = env
# TODO: record all the rewards at the SMDP level?
self.cumulant = lambda: 0
super().__init__(target_policy=target_policy,
termination=lambda: 1,
eligibility=lambda: 1,
cumulant=self.cumulant,
feature=feature_generator,
behavioural_policy=target_policy,
weights=weights,
id=repr(self))
self.options = self.Ω = options
self.option_idx_dict = {str(o): i for i, o in enumerate(self.options)}
self.lr = lr
self.gamma = gamma
self.logger = ProjectLogger(level=loglevel, printing=False)
def advantage(self, state):
""" Used as an unbiased estimator with reduced variance. """
Q = self.predict(state)
return Q - np.dot(self.π.pmf(state, Q), Q)
def utility(self, state, option: Option) -> float:
""" Utility of persisting with the same option vs picking another. """
ω = self.option_idx_dict[str(option)]
β = option.β.pmf(state)
Q = self.predict(state)
continuation_value = (1 - β) * Q[ω]
termination_value = β * np.dot(self.π.pmf(state, Q), Q)
return continuation_value + termination_value
def loss(self,
s0: np.ndarray,
o: Option,
r: float,
s1: np.ndarray,
done: bool):
""" Calculates an Intra-Option Q-learning loss """
# TODO: rewrite in terms of experience instead
γ = self.gamma
ω = self.option_idx_dict[str(o)]
δ = r - self.predict(s0)[ω]
if not done:
δ += γ * self.utility(s1, o)
return δ
def update(self, experience: Transition):
raise NotImplementedError
def learn_option_values(self, render: bool = False):
env = self.env.unwrapped
state = self.env.reset()
executing_option = self.target_policy(state)
done = False
while not done:
# Step through environment
a = executing_option.policy(state)
s_next, reward, done, info = self.env.step(a)
action_name = list(env.actions)[a].name
# TODO: structure experience in (s, a, r, s') tuples
self.logger.debug(f"State: {state}, "
f"Option: {executing_option}, "
f"Action: {action_name}, "
f"Next State: {s_next}")
if render:
self.env.render()
time.sleep(0.05)
# Update option-values for every option consistent with `a`
for option in self.options:
if option.policy(state) == a:
self.update(state, reward, s_next, option, done)
# Terminate the option
if executing_option.termination(s_next) or done:
executing_option = self.target_policy(s_next)
# Reset the state
state = s_next
return self.env.step_count
class OptionCriticNetwork:
"""
Here the weights for both critic and actor are learned by a single NN
with multiple heads.
"""
def __init__(
self,
env: MiniGridEnv,
feature_generator: NatureConvBody,
critic: IntraOptionDeepQLearning,
actor: PolicyOverOptions,
optimizer: torch.optim.Optimizer,
gamma: float = 0.99,
loglevel: int = 20,
rng: np.random.RandomState = np.random.RandomState(1),
):
self.env = env
self.γ = gamma
# shared between both critic and actor
self.feature_generator = feature_generator
self.critic = critic
self.actor = actor
# self.network = torch.nn.Sequential(self.feature_generator, self.critic.critic)
self.target_network = deepcopy(self.critic.critic)
self.optimizer = optimizer
self.logger = ProjectLogger(level=loglevel)
self.rng = rng
# TODO: check if the weights of the `network` change
# self.target_network.load_state_dict(self.network.state_dict())
def learn(self, config):
# Trackers
cumulant = 0.
duration = 0
option_switches = 0
avgduration = 0.
# Initialize
s0 = self.env.reset()
s0 = f.normalize(s0, dim=(2, 3))
φ0 = self.feature_generator(s0)
Q = self.critic(φ0)
option = self.actor(φ0, action_values=Q)
ω = self.actor.option_idx_dict[str(option)]
# Run until episode termination
done = False
while not done:
π = option.π(φ0)
# print(π)
dist = torch.distributions.Categorical(probs=π)
action, entropy = dist.sample(), dist.entropy()
s1, r, done, info = self.env.step(int(action))
s1 = f.normalize(s1, dim=(2, 3))
φ1 = self.feature_generator(s1)
# with torch.no_grad():
target_Q = self.critic(φ1)
β = option.β(φ1)
experience = TorchTransition(s0=s0,
o=option,
r=r,
s1=s1,
done=done,
φ0=φ0,
φ1=φ1,
Q=Q,
target_Q=target_Q,
π=π,
β=β)
q_loss = self.critic.loss(experience).mean()
critique = self.critic.estimate_advantage(experience)
pi_loss = -(torch.log(π)[:, action] *
critique.detach()) - config.entropy_weight * entropy
pi_loss = pi_loss.mean()
termination_advantage = self.critic.advantage(φ1, Q)[:, ω]
beta_loss = (β * (termination_advantage.detach() + config.η) *
(1 - done)).mean()
self.optimizer.zero_grad()
# print(pi_loss + q_loss + beta_loss)
# print(pi_loss, q_loss, beta_loss, '\n')
(pi_loss + q_loss + beta_loss).mean().backward(retain_graph=True)
torch.nn.utils.clip_grad_norm_(self.feature_generator.parameters(),
config.gradient_clip)
torch.nn.utils.clip_grad_norm_(self.critic.critic.parameters(),
config.gradient_clip)
torch.nn.utils.clip_grad_norm_(option.π.parameters(),
config.gradient_clip)
torch.nn.utils.clip_grad_norm_(option.β.parameters(),
config.gradient_clip)
self.optimizer.step()
# self.plot_grad_flow(self.feature_generator.named_parameters())
# plt.show()
# plt.close()
# self.plot_grad_flow(self.critic.critic.named_parameters())
# plt.show()
# plt.close()
# Choose another option in case the current one terminates
if β > self.rng.uniform():
option = self.actor(φ1, action_values=Q)
ω = self.actor.option_idx_dict[str(option)]
option_switches += 1
avgduration += (1. / option_switches) * (duration -
avgduration)
duration = 0
s0 = s1
φ0 = φ1
Q = target_Q
if self.env.step_count % 1000 == 0:
print(self.env.step_count, self.env)
# if self.env.step_count % config.target_network_update_freq == 0:
# self.target_network.load_state_dict(
# self.critic.critic.state_dict())
cumulant += r
duration += 1
self.logger.info(f'steps {self.env.unwrapped.step_count}\n'
f'cumulant {round(cumulant, 2)}\n'
f'avg. duration {round(avgduration, 2)}\n'
f'switches {option_switches}\n'
f'critic lr {self.critic.lr.rate}\n'
f'')
def plot_grad_flow(self, named_parameters):
'''Plots the gradients flowing through different layers in the net during training.
Can be used for checking for possible gradient vanishing / exploding problems.
Usage: Plug this function in Trainer class after loss.backwards() as
"plot_grad_flow(self.model.named_parameters())" to visualize the gradient flow'''
ave_grads = []
max_grads = []
layers = []
for n, p in named_parameters:
if (p.requires_grad) and ("bias" not in n):
layers.append(n)
ave_grads.append(p.grad.abs().mean())
max_grads.append(p.grad.abs().max())
plt.bar(np.arange(len(max_grads)),
max_grads,
alpha=0.1,
lw=1,
color="c")
plt.bar(np.arange(len(max_grads)),
ave_grads,
alpha=0.1,
lw=1,
color="b")
plt.hlines(0, 0, len(ave_grads) + 1, lw=2, color="k")
plt.xticks(range(0, len(ave_grads), 1), layers, rotation="vertical")
plt.xlim(left=0, right=len(ave_grads))
plt.ylim(bottom=-0.001,
top=0.02) # zoom in on the lower gradient regions
plt.xlabel("Layers")
plt.ylabel("average gradient")
plt.title("Gradient flow")
plt.grid(True)
plt.legend([
Line2D([0], [0], color="c", lw=4),
Line2D([0], [0], color="b", lw=4),
Line2D([0], [0], color="k", lw=4)
], ['max-gradient', 'mean-gradient', 'zero-gradient'])
from hrl.frameworks.options.hard_coded_options import HallwayOption, PrimitiveOption
from hrl.project_logger import ProjectLogger
from hrl.utils import cache
from hrl.visualization.plotter_one_hot import PlotterOneHot
""" Evaluate the benefits of planning with options. """
SAVEPATH = Path(f'{EXPERIMENT_DIR}/SMDP_planning')
if __name__ == '__main__':
# Create environment
env = FullyObsWrapper(FourRooms(goal_pos=(15, 15)))
# Create loggers
LOGLEVEL = 20
logger = ProjectLogger(level=LOGLEVEL, printing=False)
logger.critical(env)
plotter = PlotterOneHot(env=env)
SAVEPATH /= env.unwrapped.__class__.__name__
SAVEPATH.mkdir(parents=True, exist_ok=True)
# Create hard-coded options
options = [
HallwayOption(o, env.observation_space.shape[::-1])
for o in sorted(HallwayOption.hallway_options)
]
options += [
PrimitiveOption(o, env.observation_space.shape[::-1])
for o in sorted(PrimitiveOption.primitive_options)
]
))
return options
if __name__ == '__main__':
# Create environment
tasks = iter([(15, 15), (10, 17), (17, 10)])
env = OneHotObsWrapper(
SimplifyActionSpace(FourRooms(agent_pos=(1, 1), goal_pos=next(tasks))))
env.unwrapped.max_steps = 1000000
# env.step = partial(stochastic_step, env)
# Set up loggers
loglevel = 10
logger = ProjectLogger(level=loglevel, printing=False)
plotter = PlotterOneHot(env)
# db = redis.StrictRedis(port=6379)
# Create options
rng = np.random.RandomState(1338)
n = 8
options = create_options(env, n=n, rng=rng)
# Define actors
actor = EgreedyPolicy(ε=0.02, rng=rng, options=options, loglevel=20)
# Define critics
α = LearningRate(start_rate=0.5, min_rate=0.4, decay=0.5 / 10000)
class Feature:
class IntraOptionModelLearning:
def __init__(self,
env: MiniGridEnv,
options: Option,
loglevel: int = 20):
self.env = env
self.options = options
self.option_names_dict = {o.name: o for o in self.options}
self.option_idx_dict = {name: i for i, name in
enumerate(self.option_names_dict)}
self.logger = ProjectLogger(level=loglevel, printing=False)
def __str__(self):
return 'IntraOptionModelLearning'
def choose_option(self, state):
""" Picks an option at random """
options = [o for o in self.options if o.initiation_set[state] == 1]
return random.choice(options)
def run_episode(self,
N: np.ndarray = None,
R: np.ndarray = None,
P: np.ndarray = None,
γ: float = 0.9,
α: float = None,
render: bool = False):
env = self.env.unwrapped
n_options = len(self.options)
state_space = (4, env.width, env.height)
dim = (n_options, *state_space)
if R is None:
R = np.zeros(dim)
N = np.zeros(dim)
P = np.zeros((n_options, *state_space, *state_space))
state = self.env.reset()
done = False
executing_option = None
while not done:
if executing_option is None:
executing_option = self.choose_option(state)
a = executing_option.policy(state)
s_next, reward, done, info = self.env.step(a)
if render:
action_name = list(env.actions)[a].name
self.logger.debug(f"State: {state}, "
f"Option: {executing_option}, "
f"Action: {action_name}, "
f"Next State: {s_next}")
self.env.render()
time.sleep(0.05)
# Update model for every option consistent with last action taken
for option in self.options:
if option.policy(state) != a:
continue
o = self.option_idx_dict[executing_option.name]
option_state = (o, *state)
# Update visitation counter
if α is None:
N[option_state] += 1
alpha = 1 / N[option_state]
else:
alpha = α
# Update reward matrix
β = option.termination_function(s_next)
target = reward + γ * (1 - β) * R[(o, *s_next)]
R[option_state] += alpha * (target - R[option_state])
# Update probability transition matrix
target = γ * (1 - β) * P[o, :, :, :, s_next[0], s_next[1],
s_next[2]]
P[option_state] += alpha * (target - P[option_state])
P[(o, *state, *s_next)] += alpha * γ * β
if executing_option.termination_function(s_next) == 1:
executing_option = None
state = s_next
yield N, R, P
return N, R, P
class OptionCriticAgent:
""" Actor-Critic style agent for learning options in a differentiable way.
Architecture Overview:
The option policies parameterized by θ, termination functions
parameterized by υ and policy over options µθ belong to the actor part
of the system while the critic consists of Q_U (action-value upon entering)
and A_Ω (advantages of the policy over options).
Policy over options is learned via Intra-Option Q-Learning, but could
also be learned using policy gradients at SMDP level.
The algorithm uses variations of Policy Gradients theorem, to learn option's
policies and termination functions from a single stream of experience.
"""
# TODO: add deliberation cost
def __init__(self,
env: MiniGridEnv,
critic: IntraOptionQLearning,
actor: PolicyOverOptions,
action_critic: IntraOptionActionLearning = None,
gamma: float = 0.99,
loglevel: int = 20):
self.env = env
self.γ = gamma
self.critic = critic
self.actor = actor
self.logger = ProjectLogger(level=loglevel)
if action_critic is not None:
self.advantage_estimator = None
self.action_critic = action_critic
else:
self.advantage_estimator = 'io'
self.logger.info(f'Action-critic was not provided, '
f'so the advantages for PG would be estimated')
def estimate_advantages(self, state, option: Option, reward: float,
s_next):
ω = self.actor.option_idx_dict[str(option)]
if self.advantage_estimator == 'io':
# Intra-Option Advantage Estimator
Q = self.critic(s_next)[ω]
advantage = reward + self.γ * Q - self.critic(state)[ω]
elif self.advantage_estimator == 'augmented':
# Augmented Advantage Estimator
# FIXME: utility should be calculated wrt to the next option!
U = self.critic.utility(state, option)
advantage = reward + self.γ * U - self.critic(state)[ω]
else:
raise ValueError(f'Unknown estimator {self.advantage_estimator}')
return advantage
def learn(self, baseline: bool = False, render: bool = False):
# Trackers
cumulant = 0.
duration = 0
option_switches = 0
avgduration = 0.
# Initialize s0 and pick an option
s0 = self.env.reset()
option = self.actor(s0, action_values=self.critic(s0))
ω = self.actor.option_idx_dict[str(option)]
# Run until episode termination
done = False
while not done:
if render:
self.env.render()
time.sleep(0.05)
# Take action (a), observe next state (s1) and reward (r)
a = option.π(s0)
s1, r, done, _ = self.env.step(a)
experience = Transition(s0, option, r, s1, done)
# Option evaluation step
self.critic.update(experience)
if self.advantage_estimator is None:
self.action_critic.update(s0, a, r, s1, option, done)
# Option improvement step
if not isinstance(option.π, PrimitivePolicy):
if self.advantage_estimator is None:
critique = self.action_critic(s0, ω, a)
if baseline:
critique -= self.critic(s0)[ω]
else:
critique = self.estimate_advantages(s0, option, r, s1)
if critique:
option.π.update(s0, a, critique)
termination_advantage = self.critic.advantage(s1)[ω]
if termination_advantage:
option.β.update(s1, termination_advantage)
# Choose another option in case the current one terminates
if option.termination(s1):
option = self.actor(s1, action_values=self.critic(s1))
ω = self.actor.option_idx_dict[str(option)]
option_switches += 1
avgduration += (1. / option_switches) * (duration -
avgduration)
duration = 0
s0 = s1
cumulant += r
duration += 1
self.logger.info(f'steps {self.env.unwrapped.step_count}\n'
f'cumulant {round(cumulant, 2)}\n'
f'avg. duration {round(avgduration, 2)}\n'
f'switches {option_switches}\n'
f'critic lr {self.critic.lr.rate}\n'
f'')
# ReseedWrapper
env = Torch(FullyObsWrapper(SimplifyActionSpace(env)))
# env.step = partial(stochastic_step, env)
return env
env = setup_env(FourRooms(goal_pos=tasks.pop(0)))
env.unwrapped.max_steps = 1000000
obs = env.reset()
n_states = env.observation_space
n_actions = env.action_space.n + 1
# Set up loggers
# TODO: use RLlog
loglevel = 20
logger = ProjectLogger(level=loglevel, printing=False)
plotter = PlotterOneHot(env)
db = redis.StrictRedis(port=6379)
logger.critical(env)
# Define a network shared across options' policies and terminations,
# as well as the critic
net = NatureConvBody(in_channels=3)
params = [net.parameters()]
# Create options
rng = np.random.RandomState(1338)
n_options = 8
options, options_params = create_options(n_options, net.feature_dim,
env.action_space.n)
from hrl.frameworks.options.intra_option import IntraOptionValueLearning
from hrl.project_logger import ProjectLogger
from hrl.visualization import PlotterOneHot
SAVEPATH = Path(f'{EXPERIMENT_DIR}/value_learning')
if __name__ == '__main__':
# Create environment
tasks = iter([(15, 15), (10, 17), (17, 10), (17, 1), (8, 8)])
env = FullyObsWrapper(FourRooms(goal_pos=next(tasks)))
env.unwrapped.max_steps = 1000000
# Create loggers
LOGLEVEL = 10
logger = ProjectLogger(level=LOGLEVEL, printing=False)
logger.critical(env)
plotter = PlotterOneHot(env=env)
SAVEPATH /= env.unwrapped.__class__.__name__
SAVEPATH.mkdir(parents=True, exist_ok=True)
# Create hard-coded options
options = [
HallwayOption(o, env.observation_space.shape[::-1])
for o in sorted(HallwayOption.hallway_options)
]
options += [
PrimitiveOption(o, env.observation_space.shape[::-1])
for o in sorted(PrimitiveOption.primitive_options)
]
