def mlp_actor()

in safe_rl/sac/sac.py [0:0]

• 9 lines of code
• 1 McCabe index (conditional complexity)

def mlp_actor(x, a, name='pi', hidden_sizes=(256,256), activation=tf.nn.relu,
              output_activation=None, policy=mlp_gaussian_policy, action_space=None):
    # policy
    with tf.variable_scope(name):
        mu, pi, logp_pi = policy(x, a, hidden_sizes, activation, output_activation)
        mu, pi, logp_pi = apply_squashing_func(mu, pi, logp_pi)

    # make sure actions are in correct range
    action_scale = action_space.high[0]
    mu *= action_scale
    pi *= action_scale

    return mu, pi, logp_pi