# RL take action and get next state and reward
_, next_state_index, reward, done = env.step(action)
# RL choose action based on next state
next_action = RL.choose_action(str(next_state_index))
# RL learn from this transition (s, a, r, s, a) ==> Sarsa
RL.learn(str(state), action, reward, str(next_state_index), next_action)
# swap state and action
state = next_state_index
action = next_action
# break while loop when end of this episode
if done:
break
# end of game
print('game over')
env.destroy()
if __name__ == "__main__":
env = GridWorld()
RL = Sarsa(actions=list(range(env.n_actions)))
env.after(10000, update)
env.mainloop()
print(RL.q_table)
_, next_state_index, reward, done = env.step(action)
env.render()
if next_state_index != 'terminal':
next_i, next_j = next_state_index
else:
next_i, next_j = 0, 0
# Record value function q(s,a) = r + γ*v(s')
values.append(reward +
reward_decay * value[next_i, next_j])
# According to the optimal equation of behrman, find the maximum value function, and update q(s,a)
new_value[i, j] = np.max(values)
# Iteration termination condition: error less than 1e-4
if np.sum(np.abs(new_value - value)) < 1e-4:
draw_image(np.round(new_value, decimals=2))
plt.title('$v_{*}$')
plt.show()
plt.close()
break
value = new_value
print(value)
# end of game
print('game over')
env.destroy()
env = GridWorld(grid_world_h, grid_world_w)
value = np.zeros((grid_world_h, grid_world_w))
env.after(10000, dp)
env.mainloop()
