Robotic manipulation challenges, such as grasping and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in
Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of …

In off-policy deep reinforcement learning, it is usually hard to collect sufficient successful
experiences with sparse rewards to learn from. Hindsight experience replay (HER) enables …

In multigoal reinforcement learning (RL), algorithms usually suffer from inefficiency in the
collection of successful experiences in tasks with sparse rewards. By utilizing the ideas of …

Reinforcement learning has long been advertised as the one with the capability to
intelligently mimic and understand human learning and behavior. While the upshot of the …

Curriculum learning, a machine training strategy that feeds training instances to the model
from easy to hard, has been proven to facilitate the dialogue generation task. Meanwhile …

A long-term goal of reinforcement learning agents is to be able to perform tasks in complex
real-world scenarios. The use of external information is one way of scaling agents to more …

Reinforcement learning is generally accepted to be an appropriate and successful method
to learn robot control. Symbolic action planning is useful to resolve causal dependencies …

Curriculum learning has proven highly effective to speed up training convergence with
improved performance in a variety of tasks. Researchers have been studying how a …