def test_get_action(self):
agent = DQN(
state_shape=self.env.observation_space.shape,
action_dim=self.env.action_space.n,
gpu=-1)
state = self.env.reset()
agent.get_action(state, test=False)
agent.get_action(state, test=True)
def test_train(self):
agent = DQN(
state_shape=self.env.observation_space.shape,
action_dim=self.env.action_space.n,
memory_capacity=100,
gpu=-1)
from cpprb import ReplayBuffer
replay_buffer = ReplayBuffer(
obs_dim=self.env.observation_space.shape,
act_dim=1,
size=agent.memory_capacity)
obs = self.env.reset()
for _ in range(100):
action = agent.get_action(obs)
next_obs, reward, done, _ = self.env.step(action)
replay_buffer.add(obs=obs, act=action, next_obs=next_obs, rew=reward, done=done)
if done:
next_obs = self.env.reset()
obs = next_obs
for _ in range(100):
samples = replay_buffer.sample(agent.batch_size)
agent.train(samples["obs"], samples["act"], samples["next_obs"],
samples["rew"], np.array(samples["done"], dtype=np.float64))
