Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-task curriculum learning in a complex, visual, hard-exploration
  domain: Minecraft   An important challenge in reinforcement learning is training agents that can
solve a wide variety of tasks. If tasks depend on each other (e.g. needing to
learn to walk before learning to run), curriculum learning can speed up
learning by focusing on the next best task to learn. We explore curriculum
learning in a complex, visual domain with many hard exploration challenges:
Minecraft. We find that learning progress (defined as a change in success
probability of a task) is a reliable measure of learnability for automatically
constructing an effective curriculum. We introduce a learning-progress based
curriculum and test it on a complex reinforcement learning problem (called
"Simon Says") where an agent is instructed to obtain a desired goal item. Many
of the required skills depend on each other. Experiments demonstrate that: (1)
a within-episode exploration bonus for obtaining new items improves
performance, (2) dynamically adjusting this bonus across training such that it
only applies to items the agent cannot reliably obtain yet further increases
performance, (3) the learning-progress based curriculum elegantly follows the
learning curve of the agent, and (4) when the learning-progress based
curriculum is combined with the dynamic exploration bonus it learns much more
efficiently and obtains far higher performance than uniform baselines. These
results suggest that combining intra-episode and across-training exploration
bonuses with learning progress creates a promising method for automated
curriculum generation, which may substantially increase our ability to train
more capable, generally intelligent agents.

32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int