Very recently proximal policy optimization (PPO) algorithms have been proposed as first-order optimization methods for effective reinforcement learn- ing. While PPO is inspired by the same learn- ing theory that justifies trust region policy optimization (TRPO), PPO substantially simplifies algorithm design and improves data efficiency by performing multiple epochs of clipped policy optimization from sampled data. Although clipping in PPO stands for an important new mechanism for efficient and reliable policy update, it may fail to adaptively improve learning performance in accordance with the importance of each sampled state. To address this issue, a new surrogate learning objective featuring an adaptive clipping mechanism is proposed in this paper, enabling us to develop a new algorithm, known as PPO-λ. PPO-λ optimizes policies repeatedly based on a theoretical tar- get for adaptive policy improvement. Meanwhile, destructively large policy update can be effectively prevented through both clipping and adaptive control of a hyperparameter λ in PPO-λ, ensuring high learning reliability. PPO-λ enjoys the same simple and efficient design as PPO. Empirically on several Atari game playing tasks and benchmark control tasks, PPO-λ also achieved clearly better performance than PPO.