def METHOD_NAME():
    def check_replay_buffer_size(length):
        assert ddpg.memory.limit == REPLAY_MEMORY_SIZE
        assert len(ddpg.memory.actions) == length
        assert len(ddpg.memory.rewards) == length
        assert len(ddpg.memory.terminals) == length
        assert len(ddpg.memory.observations) == length
    ddpg = DDPG(N_STATE, N_ACTION, hparam_override={"rmsize": REPLAY_MEMORY_SIZE})
    check_replay_buffer_size(SCALAR_ZERO)
    reward_t = SCALAR_ZERO
    states_t = [SCALAR_ZERO] * N_STATE
    action_t = [SCALAR_ZERO] * N_ACTION
    is_done_t = True
    ddpg.observe(reward_t, states_t, action_t, is_done_t)
    check_replay_buffer_size(SCALAR_ONE)
    # Replay Buffer Appending is disabled in non-training mode
    # size of experience must stay as the same as above
    ddpg.is_training = False
    ddpg.observe(reward_t, states_t, action_t, is_done_t)
    ddpg.observe(reward_t, states_t, action_t, is_done_t)
    check_replay_buffer_size(SCALAR_ONE)