def eval_step(self, state):
batch = [state['obs']]
ts = tf.convert_to_tensor(batch)
logits, _ = self.bot.predict(ts)
probs = softmax(logits, state['legal_actions'])[0]
best_action = np.argmax(probs)
return best_action, probs
def eval_step(self, state):
self.bot.lstm.add_data(state['obs'])
batch = [self.bot.lstm.get_data()]
ts = tf.convert_to_tensor(batch)
logits = self.bot.predict_policy(ts)
probs = softmax(logits, state['legal_actions'])[0]
best_action = np.argmax(probs)
return best_action, probs
def get_action(self, state, legal_actions):
batch = [state]
ts = tf.convert_to_tensor(batch)
logits = self.predict_policy(ts)
probs = softmax(logits, legal_actions)[0]
selected_action = np.random.choice(self.num_actions, p=probs)
return selected_action