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from functools import partial as bind
import embodied
import numpy as np
class TestDriver:
def test_episode_length(self):
agent = self._make_agent()
driver = embodied.Driver([self._make_env])
driver.reset(agent.init_policy)
seq = []
driver.on_step(lambda tran, _: seq.append(tran))
driver(agent.policy, episodes=1)
assert len(seq) == 11
def test_first_step(self):
agent = self._make_agent()
driver = embodied.Driver([self._make_env])
driver.reset(agent.init_policy)
seq = []
driver.on_step(lambda tran, _: seq.append(tran))
driver(agent.policy, episodes=2)
for index in [0, 11]:
assert seq[index]['is_first'].item() is True
assert seq[index]['is_last'].item() is False
for index in [1, 10, 12]:
assert seq[index]['is_first'].item() is False
def test_last_step(self):
agent = self._make_agent()
driver = embodied.Driver([self._make_env])
driver.reset(agent.init_policy)
seq = []
driver.on_step(lambda tran, _: seq.append(tran))
driver(agent.policy, episodes=2)
for index in [10, 21]:
assert seq[index]['is_last'].item() is True
assert seq[index]['is_first'].item() is False
for index in [0, 1, 9, 11, 20]:
assert seq[index]['is_last'].item() is False
def test_env_reset(self):
agent = self._make_agent()
driver = embodied.Driver([bind(self._make_env, length=5)])
driver.reset(agent.init_policy)
seq = []
driver.on_step(lambda tran, _: seq.append(tran))
action = {'act_disc': np.ones(1, int), 'act_cont': np.zeros((1, 6), float)}
policy = lambda carry, obs: (carry, action, {})
driver(policy, episodes=2)
assert len(seq) == 12
seq = {k: np.array([seq[i][k] for i in range(len(seq))]) for k in seq[0]}
assert (seq['is_first'] == [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]).all()
assert (seq['is_last'] == [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1]).all()
assert (seq['reset'] == [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1]).all()
assert (seq['act_disc'] == [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0]).all()
def test_agent_inputs(self):
agent = self._make_agent()
driver = embodied.Driver([self._make_env])
driver.reset(agent.init_policy)
inputs = []
states = []
def policy(carry, obs, mode='train'):
inputs.append(obs)
states.append(carry)
_, act, _ = agent.policy(carry, obs, mode)
return 'carry', act, {}
seq = []
driver.on_step(lambda tran, _: seq.append(tran))
driver(policy, episodes=2)
assert len(seq) == 22
assert states == ([()] + ['carry'] * 21)
for index in [0, 11]:
assert inputs[index]['is_first'].item() is True
for index in [1, 10, 12, 21]:
assert inputs[index]['is_first'].item() is False
for index in [10, 21]:
assert inputs[index]['is_last'].item() is True
for index in [0, 1, 9, 11, 20]:
assert inputs[index]['is_last'].item() is False
def test_unexpected_reset(self):
class UnexpectedReset(embodied.Wrapper):
"""Send is_first without preceeding is_last."""
def __init__(self, env, when):
super().__init__(env)
self._when = when
self._step = 0
def step(self, action):
if self._step == self._when:
action = action.copy()
action['reset'] = np.ones_like(action['reset'])
self._step += 1
return self.env.step(action)
env = self._make_env(length=4)
env = UnexpectedReset(env, when=3)
agent = self._make_agent()
driver = embodied.Driver([lambda: env])
driver.reset(agent.init_policy)
steps = []
driver.on_step(lambda tran, _: steps.append(tran))
driver(agent.policy, episodes=1)
assert len(steps) == 8
steps = {k: np.array([x[k] for x in steps]) for k in steps[0]}
assert (steps['reset'] == [0, 0, 0, 0, 0, 0, 0, 1]).all()
assert (steps['is_first'] == [1, 0, 0, 1, 0, 0, 0, 0]).all()
assert (steps['is_last'] == [0, 0, 0, 0, 0, 0, 0, 1]).all()
def _make_env(self, length=10):
from embodied.envs import dummy
return dummy.Dummy('disc', length=length)
def _make_agent(self):
env = self._make_env()
agent = embodied.RandomAgent(env.obs_space, env.act_space)
env.close()
return agent
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