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DQNAgent
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class DQNAgent: | |
""" ==== 中略 ==== """ | |
def play(self, episodes): | |
total_rewards = [] | |
for n in range(episodes): | |
self.epsilon = 1.0 - min(0.95, self.global_steps * 0.95 / 500) | |
total_reward = self.play_episode() | |
total_rewards.append(total_reward) | |
print(f"Episode {n}: {total_reward}") | |
print(f"Current experiences {len(self.experiences)}") | |
print(f"Current epsilon {self.epsilon}") | |
print() | |
return total_rewards | |
def play_episode(self): | |
total_reward = 0 | |
episode_steps = 0 | |
done = False | |
state = self.env.reset() | |
while not done: | |
action = self.sample_action(state) | |
next_state, reward, done, info = self.env.step(action) | |
total_reward += reward | |
exp = Experience(state, action, reward, next_state, done) | |
self.experiences.append(exp) | |
state = next_state | |
episode_steps += 1 | |
self.global_steps += 1 | |
self.update_qnetwork() | |
#: target_networkの更新 | |
if self.global_steps % 250 == 0: | |
self.target_network.set_weights(self.q_network.get_weights()) | |
return total_reward |
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