def __init__(self, observations):
# encode observation data as int values
self.state_action_encoder = StateActionEncoder(observations)
self.state_action_encoder.observations_to_int()
dimensions = self.state_action_encoder.parse_dimensions()
# create reward, transition, and policy parsers
self.reward_parser = RewardParser(observations, dimensions)
self.transition_parser = TransitionParser(observations, dimensions)
self.policy_parser = PolicyParser(dimensions)
class MarkovAgent:
def __init__(self, observations):
# encode observation data as int values
self.state_action_encoder = StateActionEncoder(observations)
# encoding json coded data -> int
self.state_action_encoder.observations_to_int()
dimensions = self.state_action_encoder.parse_dimensions()
print dimensions
# create reward, transition, and policy parsers
self.reward_parser = RewardParser(observations, dimensions)
self.transition_parser = TransitionParser(observations, dimensions)
self.policy_parser = PolicyParser(dimensions)
def learn(self):
# calculating average rewards of each state
# average of each state = total rewards of each state / total visits of each state
R = self.reward_parser.rewards()
# calculating the probability of each state->action
# probablities of each state->action = transition counts of each state->action / total transitions
P = self.transition_parser.transition_probabilities()
# learn int-encoded policy and convert to readable dictionary
encoded_policy = self.policy_parser.policy(P, R)
self.policy = self.state_action_encoder.parse_encoded_policy(
encoded_policy)
def __init__(self, observations):
# encode observation data as int values
self.state_action_encoder = StateActionEncoder(observations)
self.state_action_encoder.observations_to_int()
dimensions = self.state_action_encoder.parse_dimensions()
# create reward, transition, and policy parsers
self.reward_parser = RewardParser(observations, dimensions)
self.transition_parser = TransitionParser(observations, dimensions)
self.policy_parser = PolicyParser(dimensions)
class MarkovAgent:
def __init__(self, observations):
# encode observation data as int values
self.state_action_encoder = StateActionEncoder(observations)
self.state_action_encoder.observations_to_int()
dimensions = self.state_action_encoder.parse_dimensions()
# create reward, transition, and policy parsers
self.reward_parser = RewardParser(observations, dimensions)
self.transition_parser = TransitionParser(observations, dimensions)
self.policy_parser = PolicyParser(dimensions)
def learn(self):
R = self.reward_parser.rewards()
P = self.transition_parser.transition_probabilities()
# learn int-encoded policy and convert to readable dictionary
encoded_policy = self.policy_parser.policy(P, R)
self.policy = self.state_action_encoder.parse_encoded_policy(encoded_policy)
class MarkovAgent:
def __init__(self, observations):
# encode observation data as int values
self.state_action_encoder = StateActionEncoder(observations)
self.state_action_encoder.observations_to_int()
dimensions = self.state_action_encoder.parse_dimensions()
# create reward, transition, and policy parsers
self.reward_parser = RewardParser(observations, dimensions)
self.transition_parser = TransitionParser(observations, dimensions)
self.policy_parser = PolicyParser(dimensions)
def learn(self):
R = self.reward_parser.rewards()
P = self.transition_parser.transition_probabilities()
# learn int-encoded policy and convert to readable dictionary
encoded_policy = self.policy_parser.policy(P, R)
self.policy = self.state_action_encoder.parse_encoded_policy(
encoded_policy)
