class Strategy:
def __init__(self, γ, α, λ, ε, ε_decay, actions):
self.γ = γ
self.α = α
self.λ = λ
self.ε = ε
self.ε_decay = ε_decay
self.actions = actions
self.eligibility_traces = None
self.q_values = QValues(actions)
self.scores = [] # TODO
self.episode = 0
self.episode_reward = 0
self.episode_reward_total = 0 # TODO
def new_episode(self):
self.eligibility_traces = EligibilityTraces(1 - self.γ * self.λ)
self.ε *= self.ε_decay
self.episode += 1
self.episode_reward = 0
def next_action(self, state, ε=None):
return self.q_values.get_greedy_action(state,
self.ε if ε is None else ε)
def update(self, state_before, action, reward, state_after):
expected_reward = self.q_values.get_expected_reward(
state_before, action)
next_action = self.q_values.get_greedy_action(state_after, self.ε)
next_expected_reward = self.q_values.get_expected_reward(
state_after, next_action)
td_error = reward - expected_reward + self.γ * next_expected_reward
self.eligibility_traces.increment(state_before, action)
self.q_values.ensure_exists(state_before, action)
def update_q_values(state, action):
old_expected_reward = self.q_values.get_expected_reward(
state, action)
new_expected_reward = old_expected_reward + self.α * td_error * self.eligibility_traces.get(
state, action)
self.q_values.set_expected_reward(state, action,
new_expected_reward)
self.eligibility_traces.decay(state, action)
self.q_values.for_each(update_q_values)
self.episode_reward += reward
def load(self, values):
self.q_values.set_all_values(values['q'])
self.ε = values['ε']
self.scores = values['scores']
self.episode = values['episode']
def dump(self):
return {
'q': self.q_values.get_all_values(),
'ε': self.ε,
'scores': self.scores,
'episode': self.episode
}
