def visualize_step_by_step(mdp, gamma, max_iter_number, min_difference):
fig = plt.figure(figsize=(5, 5))
state_values = {state: 0 for state in mdp.get_all_states()}
for i in range(max_iter_number):
new_state_values, done = rl_value_iteration(mdp, gamma, 1, min_difference, state_values)
if done:
break
draw_policy(mdp, new_state_values, fig)
state_values = new_state_values
def mass_gaming(mdp, gamma, num_iter, games_number, steps_number):
state_values = {state: 0 for state in mdp.get_all_states()}
state_values, _ = rl_value_iteration(mdp, gamma, num_iter, min_difference, state_values)
total_rewards = []
for game_i in range(games_number):
s = mdp.reset()
rewards = []
for t in range(steps_number):
s, r, done, _ = mdp.step(get_optimal_action(mdp, state_values, s, gamma))
rewards.append(r)
if done:
break
total_rewards.append(np.sum(rewards))
print('Average reward: ', np.mean(total_rewards))
if mdp.slip_chance == 0:
assert (1.0 <= np.mean(total_rewards) <= 1.0)
else:
assert (0.8 <= np.mean(total_rewards) <= 0.95)
print('Well done!')
if __name__ == '__main__':
visualize = True
mdp = FrozenLakeEnv(map_name='8x8', slip_chance=0.1)
mdp.render()
gamma = 0.9
num_iter = 100
min_difference = 1e-5
# Play in Frozen Lake Env
state_values = {state: 0
for state in mdp.get_all_states()
} # Initialize state_values
# Run value iteration algo!
state_values, _ = rl_value_iteration(mdp, gamma, num_iter, min_difference,
state_values)
# See how our agent performs - e.g. render what is going on when agent choose `optimal` value
s = mdp.reset()
mdp.render()
rewards = [] # Save all rewards to see mean reward.
for _ in range(num_iter):
action = get_optimal_action(mdp, state_values, s, gamma)
new_state, reward, done, _ = mdp.step(action)
rewards += [reward]
s = new_state
mdp.render()
if done:
break