def update_q(self, q, state, action, reward, next_state, next_action, eta,
gamma):
if next_state == agent.GOAL:
q[state, action] += eta * (reward - q[state, action])
else:
q[state,
action] += eta * (reward + gamma * q[next_state, next_action] -
q[state, action])
return q
if __name__ == '__main__':
[states, actions] = agent.THETA_0.shape
q_0 = np.random.rand(states, actions) * agent.THETA_0 * 0.1
pi_0 = agent.simple_convert_theta_to_pi(agent.THETA_0)
eta = 0.1
gamma = 0.9
epsilon = 0.5
sarsaAgent = SarsaAgent(pi_0, eta, gamma, epsilon)
q, state_action_history, state_values_history = sarsaAgent.solve(q_0)
print(q)
print(state_action_history)
maze = Maze(plt, agent.WIDTH, agent.HEIGHT, agent.WALLS, agent.START,
agent.GOAL)
# anim = maze.animate_state_history([s for s, a in state_action_history])
anim = maze.animate_state_values_history(state_values_history)
plt.show()
