def epsilon_greedy_policy_sarsa(env, state, sarsa, epsilon):
    '\n    epsilon greedy policy for q learning to generate actions\n    '
    if (random.uniform(0, 1) < epsilon):
        return env.action_space.sample()
    else:
        return np.argmax(sarsa[state])
epsilon greedy policy for q learning to generate actions
def epsilon_greedy_policy_sarsa(env, state, sarsa, epsilon):
    '\n    \n    '
    if (random.uniform(0, 1) < epsilon):
        return env.action_space.sample()
    else:
        return np.argmax(sarsa[state])