def main():
# Set log level.
logging.basicConfig(level=logging.DEBUG)
# Set a random seed for consistency in agent AND environment.
if config.RANDOM_SEED is not None:
np.random.seed(config.RANDOM_SEED)
# Make environment.
env = EnvCatcher(grid_size=config.GRID_SIZE,
env_type='episodic',
verbose=False,
random_seed=config.RANDOM_SEED)
# Make agent.
agent = SarsaAgent(actions=list(range(env.action_space)),
learning_rate=config.LEARNING_RATE,
discount_factor=config.DISCOUNT_FACTOR,
epsilon=config.EPSILON)
# Run the RL Loop.
episode_returns = rl.run_loop(agent=agent,
env=env,
max_num_steps=config.MAX_NUM_STEPS,
report_every_n=config.REPORT_EVERY_N)
# Save the data.
date_string = time.strftime("%Y%m%d-%H%M%S")
filename = ('sarsa_grid_{}_nep_{}_'.format(
config.GRID_SIZE, len(episode_returns)) + date_string + '.csv')
rl.save_episode_returns(filename=filename, episode_returns=episode_returns)
def runTest(self):
logging.info('SarsaAgentTest')
ep_returns = rl.run_loop(self.agent, self.env, (GRID_SIZE-1)*N_EPS)
self.assertEqual(len(ep_returns), N_EPS)
# Test for learning.
self.assertEqual(0.2,np.average(ep_returns[-REPORT_EVERY_N:]))
def runTest(self):
logging.info('RandomAgentTest')
ep_returns = rl.run_loop(self.agent, self.env, (GRID_SIZE-1)*N_EPS)
self.assertEqual(len(ep_returns), N_EPS)