def inplace_sub(x, i, v): """Applies an inplace sub on input x at index i with value v. Note that this function is not actually inplace - it allocates a copy of x. The utility is not avoiding memory copies but rather specifying a sparse update. If i is None, x and v must be the same shape. Computes y = x; y -= v; If i is a scalar, x has a rank 1 higher than v's. Computes y = x; y[i, :] -= v; Otherwise, x and v must have the same rank. Computes y = x; y[i, :] -= v; Args: x: A Tensor. i: None, a scalar or a vector. v: A Tensor. Returns: Returns y, which is guaranteed not to be an alias of x. """ return alias_inplace_sub(gen_array_ops.deep_copy(x), i, v)
def inplace_sub(x, i, v): """Applies an inplace sub on input x at index i with value v. Note that this function is not actually inplace - it allocates a copy of x. The utility is not avoiding memory copies but rather specifying a sparse update. If i is None, x and v must be the same shape. Computes y = x; y -= v; If i is a scalar, x has a rank 1 higher than v's. Computes y = x; y[i, :] -= v; Otherwise, x and v must have the same rank. Computes y = x; y[i, :] -= v; Args: x: A Tensor. i: None, a scalar or a vector. v: A Tensor. Returns: Returns y, which is guaranteed not to be an alias of x. """ return alias_inplace_sub(gen_array_ops.deep_copy(x), i, v)
model(np.ones([1, 3])) # model = keras.models.load_model("cartpole_model_with_TD(lambda)_60000steps.h5") optimizer = keras.optimizers.RMSprop(learning_rate=0.01, momentum=0.95, rho=0.95) model.summary() programing_times = 10 replay_memory_length = 1000 replay_memory = [] gamma = 0.99 old_theta = [] for each in model.trainable_variables: old_theta.append(deep_copy(each)) max_episode_num = 10 for episode in range(max_episode_num): print(f'Episode: {episode+1}') t = 0 state = env.reset() step = 0 rewards = 0 while True: action = greedy_action(model, state) state_next, reward, done, _ = env.step(action) if done and step < 300: