Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bootstrapped Q-learning with Context Relevant Observation Pruning to
  Generalize in Text-based Games   We show that Reinforcement Learning (RL) methods for solving Text-Based Games
(TBGs) often fail to generalize on unseen games, especially in small data
regimes. To address this issue, we propose Context Relevant Episodic State
Truncation (CREST) for irrelevant token removal in observation text for
improved generalization. Our method first trains a base model using Q-learning,
which typically overfits the training games. The base model's action token
distribution is used to perform observation pruning that removes irrelevant
tokens. A second bootstrapped model is then retrained on the pruned observation
text. Our bootstrapped agent shows improved generalization in solving unseen
TextWorld games, using 10x-20x fewer training games compared to previous
state-of-the-art methods despite requiring less number of training episodes.

25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph