def train(env=None):
agent = SARSAAgent()
if env is None:
env = gym.make("FrozenLakeEasy-v0")
agent.learn(env, episode_count=500)
show_q_value(agent.Q)
agent.show_reward_log()
def train(env=None):
agent = MonteCarloAgent(epsilon=0.1)
if env is None:
env = gym.make("FrozenLakeEasy-v0")
agent.learn(env, episode_count=500)
show_q_value(agent.Q)
agent.show_reward_log()
def train(env=None):
trainer = ActorCritic(Actor, Critic)
if env is None:
env = gym.make("FrozenLakeEasy-v0")
actor, _ = trainer.train(env, episode_count=3000)
print(actor.Q)
show_q_value(actor.Q)
actor.show_reward_log()
def train():
agent = QLearningAgent()
env = gym.make("FrozenLakeEasy-v0")
# env.render()
agent.learn(env,
episode_count=300000,
render=False,
gamma=gamma,
report_interval=100000)
show_q_value(agent.Q)
agent.show_reward_log()
def train_gamma(env, name, gamma):
agent = QLearningAgent()
# env.render()
agent.learn(env,
episode_count=300000,
render=False,
gamma=gamma,
report_interval=100000)
show_q_value(agent.Q, name=name + "_stage.png")
agent.show_reward_log(name=name + "_figure.png")
print("game start")
print("gamma={0} : {1}".format(
gamma, "goal" if play(agent, env) else "game over"))
def train_stg(count=10, name=None):
for i in range(10):
print("now: {}th".format(i))
agent = QLearningAgent()
env = gym.make("FrozenLakeEasy-v0")
env.render()
agent.learn(env,
episode_count=500000,
render=False,
report_interval=100000)
show_q_value(agent.Q, name=name + "_{}_stage.png".format(i))
agent.show_reward_log(name=name + "_{}_figure.png".format(i))
print("game start")
print("{0}th: {1}".format(i + 1,
"goal" if play(agent, env) else "game over"))
def train():
agent = QLearningAgent()
env = gym.make("FrozenLakeEasy-v0")
agent.learn(env, episode_count=500)
show_q_value(agent.Q)
agent.show_reward_log()
gain = reward + gamma * self.Q[n_state][n_action]
estimated = self.Q[s][a]
self.Q[s][a] += learning_rate * (gain - estimated)
s = n_state
if self.q_learning:
a = self.policy(s, actions)
else:
a = n_action
else:
self.log(reward)
if e != 0 and e % report_interval == 0:
pass
# self.show_reward_log(episode=e)
def train(q_learning):
env = gym.make("FrozenLakeEasy-v0")
agent = CompareAgent(q_learning=q_learning)
agent.learn(env, episode_count=100000)
return dict(agent.Q)
if __name__ == "__main__":
with Pool() as pool:
results = pool.map(train, ([True, False]))
for r in results:
show_q_value(r)
def train():
trainer = ActorCritic(Actor, Critc)
env = gym.make("FrozenLakeEasy-v0")
actor, critic = trainer.train(env, episode_count=3000)
show_q_value(actor.Q)
actor.show_reward_log()
def train():
agent = SARSAAgent()
env = gym.make("FrozenLakeEasy-v0")
agent.learn(env)
show_q_value(agent.Q)
agent.show_reward_log()
def train():
agent = MonteCarloAgent(epsilon=0.1)
env = gym.make("FrozenLakeEasy-v0")
agent.learn(env)
show_q_value(agent.Q)
agent.show_reward_log()
