def main(argv):
del argv
gw = Gridworld(10, 10, 0, 99)
#for i in range(7):
# gw.grid[7][i] = 1
agent_module = importlib.import_module("agents." + FLAGS.agent)
avg_num_steps = np.zeros(FLAGS.num_episodes)
policy = getattr(policies.tabular_policies, FLAGS.policy)
for _ in range(FLAGS.num_trials):
agent = agent_module.Agent(FLAGS.agent, gw.width * gw.height,
FLAGS.gamma, policy, FLAGS.alpha)
steps_per_episode = []
for _ in range(FLAGS.num_episodes):
state = gw.start
action = agent.select_action(state)
step = 0
terminate = False
while step < FLAGS.max_steps and not terminate:
next_state = gw.apply_action(state, action)
terminate, reward = gw.is_goal(next_state)
next_action = agent.select_action(next_state)
agent.update(state, action, reward, next_state, next_action)
state = next_state
action = next_action
step += 1
steps_per_episode.append(step)
avg_num_steps += np.array(steps_per_episode)
avg_num_steps = avg_num_steps / FLAGS.num_trials
plt.plot(avg_num_steps)
plt.show()
def main(argv):
del argv
gw = Gridworld(10, 10, 0, 80)
for i in range(7):
gw.grid[7][i] = 1
agent_module = importlib.import_module("agents." + FLAGS.agent)
avg_num_steps = np.zeros(FLAGS.num_episodes)
policy = getattr(policies.tabular_policies, FLAGS.policy)
for _ in range(FLAGS.num_trials):
agent = agent_module.Agent(FLAGS.agent, gw.width * gw.height, FLAGS.n,
FLAGS.gamma, policy, FLAGS.alpha)
steps_per_episode = []
for _ in range(FLAGS.num_episodes):
T = np.Inf
state = gw.start
agent.reset_agent()
agent.stored_states.append(state)
action = agent.select_action(state)
agent.stored_actions.append(action)
step = 0
tau = 0
while tau != T - 1:
if step < T:
next_state = gw.apply_action(state, action)
terminate, reward = gw.is_goal(next_state)
agent.stored_states.append(next_state)
agent.stored_rewards.append(reward)
if terminate or step == FLAGS.max_steps - 1:
T = step + 1
else:
next_action = agent.select_action(next_state)
agent.stored_actions.append(next_action)
state = next_state
action = next_action
tau = step - agent.n + 1
if tau >= 0:
agent.update(tau, T)
step += 1
steps_per_episode.append(step)
avg_num_steps += np.array(steps_per_episode)
avg_num_steps = avg_num_steps / FLAGS.num_trials
np.save(FLAGS.log_path + "/" + FLAGS.log_file, avg_num_steps)
plt.plot(avg_num_steps)
plt.savefig(FLAGS.log_path + "/" + FLAGS.log_file)