def run_experiment(self, environment, experiment_num=0):
environment = RLgraphEnvironmentWrapper(environment)
environment.add_episode_end_callback(self.episode_finished,
environment,
runner_id=1)
config = copy(self.config)
max_episodes = config.pop('max_episodes', None)
max_timesteps = config.pop('max_timesteps', None)
max_episode_timesteps = config.pop('max_episode_timesteps')
agent = Agent.from_spec(
spec=config,
state_space=environment.state_space,
action_space=environment.action_space,
)
if experiment_num == 0 and self.load_model_file:
logging.info("Loading model data from file: {}".format(
self.load_model))
agent.load_model(self.load_model_file)
runner = SingleThreadedWorker(agent=agent, environment=environment)
environment.reset()
agent.reset_buffers()
if max_timesteps:
runner.execute_timesteps(
num_timesteps=max_timesteps,
max_timesteps_per_episode=max_episode_timesteps)
else:
runner.execute_episodes(
num_episodes=max_episodes,
max_timesteps_per_episode=max_episode_timesteps)
return dict(initial_reset_time=0,
episode_rewards=runner.episode_rewards,
episode_timesteps=runner.episode_steps,
episode_end_times=runner.episode_durations)
def test_episodes(self):
"""
Simply tests if episode execution loop works and returns a result.
"""
agent = RandomAgent(action_space=self.environment.action_space,
state_space=self.environment.state_space)
worker = SingleThreadedWorker(env_spec=lambda: self.environment,
agent=agent,
frameskip=1,
worker_executes_preprocessing=False)
result = worker.execute_episodes(5, max_timesteps_per_episode=10)
# Max 5 * 10.
self.assertLessEqual(result['timesteps_executed'], 50)
self.assertEqual(result['episodes_executed'], 5)
self.assertLessEqual(result['env_frames'], 50)
self.assertGreaterEqual(result['runtime'], 0.0)