  In this paper, a new adaptive multi-batch experience replay scheme is
proposed for proximal policy optimization (PPO) for continuous action control.
On the contrary to original PPO, the proposed scheme uses the batch samples of
past policies as well as the current policy for the update for the next policy,
where the number of the used past batches is adaptively determined based on the
oldness of the past batches measured by the average importance sampling (IS)
weight. The new algorithm constructed by combining PPO with the proposed
multi-batch experience replay scheme maintains the advantages of original PPO
such as random mini-batch sampling and small bias due to low IS weights by
storing the pre-computed advantages and values and adaptively determining the
mini-batch size. Numerical results show that the proposed method significantly
increases the speed and stability of convergence on various continuous control
tasks compared to original PPO.
