def test_to_dict_from_dict(self):
oldseed = agents.seed
agents.seed = 123
p1 = PpoAgent(gym_env_name=_line_world_name, fc_layers=(10, 20, 30), backend='tfagents')
d = p1._to_dict()
agents.seed = oldseed
p2: EasyAgent = EasyAgent._from_dict(d)
self.assert_are_equal(p1, p2)
def test_save_load_play(self):
oldseed = agents.seed
agents.seed = 123
p1 = PpoAgent(gym_env_name=_line_world_name, fc_layers=(10, 20, 30), backend='tfagents')
p1.train(callbacks=[duration._SingleEpisode()], default_plots=False)
d = p1.save()
agents.seed = oldseed
p2: EasyAgent = agents.load(d)
self.assert_are_equal(p1, p2)
p2.play(default_plots=False, num_episodes=1)
def test_train_cartpole(self):
for backend in get_backends(PpoAgent):
ppo = PpoAgent(gym_env_name="CartPole-v0", backend=backend)
tc = core.PpoTrainContext()
tc.num_iterations = 3
tc.num_episodes_per_iteration = 10
tc.max_steps_per_episode = 500
tc.num_epochs_per_iteration = 5
tc.num_iterations_between_eval = 2
tc.num_episodes_per_eval = 5
ppo.train([log.Iteration()], train_context=tc)
def test_train(self):
agents.seed = 0
for backend in get_backends(PpoAgent):
ppo = PpoAgent(gym_env_name=_cartpole_name, backend=backend)
tc = core.PpoTrainContext()
tc.num_iterations = 10
tc.num_episodes_per_iteration = 10
tc.max_steps_per_episode = 200
tc.num_epochs_per_iteration = 5
tc.num_iterations_between_eval = 5
tc.num_episodes_per_eval = 5
ppo.train([log.Iteration()], train_context=tc, default_plots=False)
assert max_avg_rewards(tc) >= 50
def test_callback_single(self):
for backend in get_backends(PpoAgent):
env._StepCountEnv.clear()
agent = PpoAgent(_env_name, backend=backend)
agent.train(duration._SingleEpisode())
assert env._StepCountEnv.reset_count <= 2
def test_save_no_trained_policy_exception(self):
p1 = PpoAgent(gym_env_name=_line_world_name, fc_layers=(10, 20, 30), backend='tfagents')
with pytest.raises(Exception):
p1.save()