def train():
model = create_model()
#将定义好的网络作为参数传入huskarl框架的API中，构成一个完成DQN 智能体，用于接下来的强化学习训练。
agent = hk.agent.DQN(model, actions=dummy_env.action_space.n, nsteps=2)
cpkt = tf.io.gfile.listdir("model_dir")
if cpkt:
agent.model.load_weights("model_dir/dqn.h5")
# 使用huskarl框架的simulation来创建一个训练模拟器，在模拟器中进行训练。
sim = hk.Simulation(create_env, agent)
sim.train(max_steps=3000, visualize=True, plot=plot_rewards)
agent.model.save_weights(filepath='model_dir/dqn.h5',
overwrite=True,
save_format='h5')
Dense(16, activation='relu'),
Dense(16, activation='relu'),
Dense(action_size, activation='linear')
])
# Build a simple critic model
action_input = Input(shape=(action_size,), name='action_input')
state_input = Input(shape=state_shape, name='state_input')
x = Concatenate()([action_input, state_input])
x = Dense(32, activation='relu')(x)
x = Dense(32, activation='relu')(x)
x = Dense(32, activation='relu')(x)
x = Dense(1, activation='linear')(x)
critic = Model(inputs=[action_input, state_input], outputs=x)
# Create Deep Deterministic Policy Gradient agent
agent = hk.agent.DDPG(actor=actor, critic=critic, nsteps=2)
def plot_rewards(episode_rewards, episode_steps, done=False):
plt.clf()
plt.xlabel('Step')
plt.ylabel('Reward')
for ed, steps in zip(episode_rewards, episode_steps):
plt.plot(np.array(steps), np.array(ed))
plt.show() if done else plt.pause(0.001) # Pause a bit so that the graph is updated
# Create simulation and start training
sim = hk.Simulation(create_env, agent)
sim.train(max_steps=30_000, visualize=True, plot=plot_rewards)
sim.test(max_steps=5_000)
def test():
model = create_model()
agent = hk.agent.DQN(model, actions=dummy_env.action_space.n, nsteps=2)
agent.model.load_weights("model_dir/dqn.h5")
sim = hk.Simulation(create_env, agent)
sim.test(max_steps=1000)
