def monte_carlo_demo():
np.random.seed(101)
env = SnakeEnv(10, [3, 6])
agent = ModelFreeAgent(env)
mc = MonteCarlo(0.5)
with timer('Timer Monte Carlo Iter'):
mc.monte_carlo_opt(agent, env)
print('return_pi={}'.format(eval_game(env, agent)))
print(agent.pi)
np.random.seed(101)
agent2 = TableAgent(env)
pi_algo = PolicyIteration()
with timer('Timer PolicyIter'):
pi_algo.policy_iteration(agent2)
print('return_pi={}'.format(eval_game(env, agent2)))
print(agent2.pi)
np.random.seed(101)
agent3 = ModelFreeAgent(env)
mc = SARSA(0.5)
with timer('Timer Monte Carlo Iter'):
mc.sarsa(agent3, env)
print('return_pi={}'.format(eval_game(env, agent3)))
print(agent3.pi)
def monte_carlo_demo():
np.random.seed(0)
env = SnakeEnv(10, [3, 6])
agent2 = TableAgent(env)
pi_algo = PolicyIteration()
with timer('Timer PolicyIter'):
pi_algo.policy_iteration(agent2)
print('PolicyIteration:return_pi={}'.format(eval_game(env, agent2)))
print(agent2.pi)
np.random.seed(0)
env = SnakeEnv(10, [3, 6])
agent3 = TableAgent(env)
vi_algo = ValueIteration()
vi_algo.value_iteration(agent3)
print('ValueIteration:return_pi={}'.format(eval_game(env, agent3)))
print(agent3.pi)
np.random.seed(0)
env = SnakeEnv(10, [3, 6])
agent = ModelFreeAgent(env)
mc = MonteCarlo()
with timer('Timer Monte Carlo Iter'):
mc.monte_carlo_opt(agent, env)
print('MonteCarlo:return_pi={}'.format(eval_game(env, agent)))
print(agent.pi)
def policy_iter_run(test_episode_num=1_000_000):
""" Run value iteration algorithm.
Parameter introduction please refer to 'policy_iter.py'
"""
model = PolicyIteration(env)
model.policy_iteration()
model.test_policy(episode_num=test_episode_num)
model.save_fig(fig_path)
model.save_result(log_path)
def monte_carlo_demo():
env = SnakeEnv(10, [3, 6])
agent = ModelFreeAgent(env)
mc = MonteCarlo()
with timer('Timer Monte Carlo Iter'):
mc.monte_carlo_opt(agent, env)
print('return_pi={}'.format(eval_game(env, agent)))
print(agent.pi)
agent2 = TableAgent(env)
pi_algo = PolicyIteration()
with timer('Timer PolicyIter'):
pi_algo.policy_iteration(agent2)
print('return_pi={}'.format(eval_game(env, agent2)))
print(agent2.pi)