user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning of Sophisticated Curriculums by viewing them as Graphs over Tasks

Paper content:
Curriculum learning consists in learning a difficult task by first training on an easy version of it, then on more and more difficult versions and finally on the difficult task.
To make this learning efficient, given a curriculum and the current learning state of an agent, we need to find what are the good next tasks to train the agent on.
Teacher-Student algorithms assume that the good next tasks are the ones on which the agent is making the fastest progress or digress.
We first simplify and improve them.
However, two problematic situations where the agent is mainly trained on tasks it can't learn yet or it already learnt may occur.
Therefore, we introduce a new algorithm using min max ordered curriculums that assumes that the good next tasks are the ones that are learnable but not learnt yet.
It outperforms Teacher-Student algorithms on small curriculums and significantly outperforms them on sophisticated ones with numerous tasks.
Curriculum learning.
An agent with no prior knowledge can learn a lot of tasks by reinforcement, i.e. by reinforcing (taking more often) actions that lead to higher reward.
But, for some very hard tasks, it is impossible.
Let's consider the following task:Figure 1: The agent (in red) receives a reward of 1 when it picks up the blue ball in the adjacent room.
To do so, it has to first open the gray box, take the key inside and then open the locked door.
This is an easy task for humans because we have prior knowledge: we know that a key can be picked up, that we can open a locked door with a key, etc...
However, most of the time, the agent starts with no prior knowledge, i.e. it starts by acting randomly. Therefore, it has a probability near 0 of achieving the task in a decent number of time-steps, so it has a probability near 0 of getting reward, so it can't learn the task by reinforcement.One solution to still learn this task is to do curriculum learning BID0 ), i.e. to first train the agent on an easy version of the task, where it can get reward and learn, then train on more and more difficult versions using the previously learnt policy and finally, train on the difficult task.Learning by curriculum may be decomposed into two parts:1. Defining the curriculum, i.e. the set of tasks the agent may be trained on. 2. Defining the program, i.e. the sequence of curriculum's tasks it will be trained on.These two parts can be done online, during training.Curriculum learning algorithms. Defining a curriculum and a program can be done manually, e.g. by defining a hand-chosen performance threshold for advancement to the next task BID6 ; BID5 ).However, if an efficient algorithm is found, it may save us a huge amount of time in the future. Besides, efficient (and more efficient than humans) algorithms are likely to exist because they can easily mix in different tasks (what is hard for humans) and then:• avoid catastrophic forgetting by continuously retraining on easier tasks;• quickly detect learnable but not learnt yet tasks.Hence, it motivates the research of curriculum learning algorithms.Curriculum learning algorithms can be grouped into two categories:1. curriculum algorithms: algorithms that define the curriculum; 2. program algorithms: algorithms that define the program, i.e. that decide, given a curriculum and the learning state of the agent, what are the good next tasks to train the agent on.In this paper, we will focus on program algorithms, in the reinforcement learning context. Recently, several such algorithms emerged, focused on the notion of learning progress BID4 ; BID3 BID2 ). BID4 proposed four algorithms (called Teacher-Student) based on the assumption that the good next tasks are the ones on which the agent is making the fastest progress or digress.We first simplify and improve Teacher-Student algorithms (section 4). However, even improved, two problematic situations where the agent is mainly trained on tasks it can't learn or it already learnt may occur. Therefore, we introduce a new algorithm (section 5), focused on the notion of mastering rate, based on the assumption that the good next tasks are the ones that are learnable but not learnt yet.We show that this algorithm outperforms Teacher-Student algorithms on small curriculums and significantly outperforms them on sophisticated ones with numerous tasks.

assistant: text: We present a new algorithm for learning by curriculum based on the notion of mastering rate that outperforms previous algorithms.