def test_train(self):
for backend in get_backends(RandomAgent):
random_agent = RandomAgent('CartPole-v0', backend=backend)
tc: core.TrainContext = random_agent.train([log.Duration(), log.Iteration()],
num_iterations=10,
max_steps_per_episode=100,
default_plots=False)
(min_steps, avg_steps, max_steps) = tc.eval_steps[tc.episodes_done_in_training]
assert avg_steps >= 10
def test_score(self):
random_agent = RandomAgent('CartPole-v0')
num_episodes = 5
mean, std, min_reward, max_reward, all_rewards = random_agent.score(num_episodes=num_episodes)
assert min_reward <= max_reward
assert mean <= max_reward
assert mean >= min_reward
assert std >= 0
assert len(all_rewards) == num_episodes
def test_train(self):
for backend in get_backends(RandomAgent):
random_agent = RandomAgent(_line_world_name, backend=backend)
tc: core.TrainContext = random_agent.train([log.Duration(), log.Iteration()],
num_iterations=10,
max_steps_per_episode=100,
default_plots=False)
r = max_avg_rewards(tc)
assert r >= 0
def test_evaluate(self):
random_agent = RandomAgent('CartPole-v0')
num_episodes = 5
metrics = random_agent.evaluate(num_episodes=num_episodes)
assert_properties_for_metric(metrics.steps, num_episodes)
assert_properties_for_metric(metrics.rewards, num_episodes)
def test_seed(self):
oldseed = agents.seed
agents.seed = 123
random_agent = RandomAgent(_line_world_name)
assert random_agent._model_config.seed == 123
agents.seed = oldseed
def test_evaluate(self):
random_agent = RandomAgent(_line_world_name)
num_episodes = 5
metrics = random_agent.evaluate(num_episodes=num_episodes)
self.assert_properties_for_metric(metrics.steps, num_episodes)
self.assert_properties_for_metric(metrics.rewards, num_episodes)
def test_agent_saver_set(self):
random_agent = RandomAgent(_line_world_name)
assert random_agent._backend_agent._agent_context._agent_saver