def setState(self, observation):
self.lstate = GridworldEnv.state2str(observation)
if self.lstate not in self.Q.keys():
self.Q[self.lstate] = np.zeros(self.nb_action)
if random.uniform(0, 1) < self.epsilon:
self.laction = np.random.randint(self.nb_action)
else:
self.laction = np.argmax([self.Q[self.lstate]])
def act(self, observation, reward, done):
obs = GridworldEnv.state2str(observation)
if obs not in self.Q.keys():
self.Q[obs] = np.zeros(self.nb_action)
if random.uniform(0, 1) < self.epsilon:
self.laction = np.random.randint(self.nb_action)
else:
self.laction = np.argmax([self.Q[obs]])
self._update_Qvalue(reward, obs, done)
self.lstate = obs
return self.laction
def _total_reward(self):
"""Sum of rewards expected for every state"""
return sum(self.value[state] for state in self.mdp.keys())
obs = GridworldEnv.state2str(observation)
if obs in self.Q.keys():
self.Q[obs] = np.zeros(self.nb_action)
if random.uniform(0, 1) < self.epsilon:
self.laction = np.random.randint(self.nb_action)
else:
self.laction = np.argmax([self.Q[obs]])
self._update_Qvalue(reward, obs, done)
self.lstate = obs
return self.laction
def setState(self, observation):
self.lstate = GridworldEnv.state2str(observation)
if self.lstate not in self.Q.keys():
self.Q[self.lstate] = np.zeros(self.nb_action)
def act(self, observation, reward, done):
# get action for current state
# obs = str(obs.tolist())
obs = GridworldEnv.state2str(observation)
action = self.policy[obs]
return action
