  On-policy deep reinforcement learning algorithms have low data utilization
and require significant experience for policy improvement. This paper proposes
a proximal policy optimization algorithm with prioritized trajectory replay
(PTR-PPO) that combines on-policy and off-policy methods to improve sampling
efficiency by prioritizing the replay of trajectories generated by old
policies. We first design three trajectory priorities based on the
characteristics of trajectories: the first two being max and mean trajectory
priorities based on one-step empirical generalized advantage estimation (GAE)
values and the last being reward trajectory priorities based on normalized
undiscounted cumulative reward. Then, we incorporate the prioritized trajectory
replay into the PPO algorithm, propose a truncated importance weight method to
overcome the high variance caused by large importance weights under multistep
experience, and design a policy improvement loss function for PPO under
off-policy conditions. We evaluate the performance of PTR-PPO in a set of Atari
discrete control tasks, achieving state-of-the-art performance. In addition, by
analyzing the heatmap of priority changes at various locations in the priority
memory during training, we find that memory size and rollout length can have a
significant impact on the distribution of trajectory priorities and, hence, on
the performance of the algorithm.
