# 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)
