In Reinforcement Learning (RL), an agent explores the environment and
collects trajectories into the memory buffer for later learning. However, the
collected trajectories can easily be imbalanced with respect to the achieved
goal states. The problem of learning from imbalanced data is a well-known
problem in supervised learning, but has not yet been thoroughly researched in
RL. To address this problem, we propose a novel Curiosity-Driven Prioritization
(CDP) framework to encourage the agent to over-sample those trajectories that
have rare achieved goal states. The CDP framework mimics the human learning
process and focuses more on relatively uncommon events. We evaluate our methods
using the robotic environment provided by OpenAI Gym. The environment contains
six robot manipulation tasks. In our experiments, we combined CDP with Deep
Deterministic Policy Gradient (DDPG) with or without Hindsight Experience
Replay (HER). The experimental results show that CDP improves both performance
and sample-efficiency of reinforcement learning agents, compared to
state-of-the-art methods.

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