def __init__(self,
actions,
gamma=.9,
r_max=1.,
v_max=None,
deduce_v_max=True,
n_known=None,
deduce_n_known=True,
epsilon_q=0.1,
epsilon_m=None,
delta=None,
n_states=None,
max_memory_size=None,
prior=None,
estimate_distances_online=True,
min_sampling_probability=.1,
name="ExpLRMax",
path='results/'):
LRMax.__init__(self,
actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=deduce_v_max,
n_known=n_known,
deduce_n_known=deduce_n_known,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_memory_size,
prior=prior,
estimate_distances_online=estimate_distances_online,
min_sampling_probability=min_sampling_probability,
name=name)
# Counters used for experiments (not useful to the algorithm)
self.n_rmax = 0 # number of times the rmax bound is used for all the updates of 1 task
self.n_lip = 0 # number of times the lipschitz bound is used for all the updates of 1 task
# Counter for prior use
self.n_prior_use = 0 # number of times the prior is used for each update of 1 task
self.n_dista_use = 0 # number of times the distance is used for each update of 1 task
# Recorded variables
self.discounted_return = 0.
self.total_return = 0.
self.n_time_steps = 0 # number of time steps
self.path = path
self.write_data = False # Enable data writing
self.instance_number = 0
self.run_number = 0
def __init__(
self,
actions,
gamma=.9,
r_max=1.,
v_max=None,
deduce_v_max=True,
n_known=None,
deduce_n_known=True,
epsilon_q=0.1,
epsilon_m=None,
delta=None,
n_states=None,
max_memory_size=None,
prior=None,
estimate_distances_online=True,
min_sampling_probability=.1,
name="LRMaxQInit"
):
"""
:param actions: action space of the environment
:param gamma: (float) discount factor
:param r_max: (float) known upper-bound on the reward function
:param v_max: (float) known upper-bound on the value function
:param deduce_v_max: (bool) set to True to deduce v_max from r_max
:param n_known: (int) count after which a state-action pair is considered known
(only set n_known if delta and epsilon are not defined)
:param deduce_n_known: (bool) set to True to deduce n_known from (delta, n_states, epsilon_m)
:param epsilon_q: (float) precision of value iteration algorithm for Q-value computation
:param epsilon_m: (float) precision of the learned models in L1 norm
:param delta: (float) models are learned epsilon_m-closely with probability at least 1 - delta
:param n_states: (int) number of states
:param max_memory_size: (int) maximum number of saved models (infinity if None)
:param prior: (float) prior knowledge of maximum model's distance
:param estimate_distances_online: (bool) set to True for online estimation of a tighter upper-bound for the
model pseudo-distances. The estimation is valid with high probability.
:param min_sampling_probability: (float) minimum sampling probability of an environment
:param name: (str)
"""
self.name = name
self.n_required_tasks = mqi.number_of_tasks_for_high_confidence_upper_bound(delta, min_sampling_probability)
self.maxQ_memory = [] # Upper-bounds on the Q-values of previous MDPs
LRMax.__init__(self, actions=actions, gamma=gamma, r_max=r_max, v_max=v_max, deduce_v_max=deduce_v_max,
n_known=n_known, deduce_n_known=deduce_n_known, epsilon_q=epsilon_q, epsilon_m=epsilon_m,
delta=delta, n_states=n_states, max_memory_size=max_memory_size, prior=prior,
estimate_distances_online=estimate_distances_online,
min_sampling_probability=min_sampling_probability, name=name)
def __init__(self,
actions,
gamma=.9,
r_max=1.,
v_max=None,
deduce_v_max=True,
n_known=None,
deduce_n_known=True,
epsilon_q=0.1,
epsilon_m=None,
delta=None,
n_states=None,
max_memory_size=None,
prior=None,
estimate_distances_online=True,
min_sampling_probability=.1,
name="ExpLRMax"):
"""
See LRMax class.
"""
LRMax.__init__(self,
actions=actions,
gamma=gamma,
r_max=r_max,
v_max=v_max,
deduce_v_max=deduce_v_max,
n_known=n_known,
deduce_n_known=deduce_n_known,
epsilon_q=epsilon_q,
epsilon_m=epsilon_m,
delta=delta,
n_states=n_states,
max_memory_size=max_memory_size,
prior=prior,
estimate_distances_online=estimate_distances_online,
min_sampling_probability=min_sampling_probability,
name=name)
self.time_step = 0
self.time_step_counter = []
self.data = {'n_computation': [0], 'n_prior_use': [0]}
