Blog
The latest from Google Research
Learning to Generalize from Sparse and Underspecified Rewards
Friday, February 22, 2019
Posted by Rishabh Agarwal, Google AI Resident and Mohammad Norouzi, Research Scientist
Reinforcement learning
(RL) presents a unified and flexible framework for optimizing goal-oriented behavior, and has enabled remarkable success in addressing challenging tasks such as
playing video games
,
continuous control
, and
robotic learning
. The success of RL algorithms in these application domains often hinges on the availability of high-quality and dense reward feedback. However, broadening the applicability of RL algorithms to environments with
sparse
and
underspecified
rewards is an ongoing challenge, requiring a
learning agent
to
generalize
(
i.e.,
learn the right behavior) from limited feedback. A natural way to investigate the performance of RL algorithms in such problem settings is via
language understanding
tasks, where an agent is provided with a natural language input and needs to generate a complex response to achieve a goal specified in the input, while only receiving binary success-failure feedback.
For instance, consider a "blind" agent tasked with reaching a goal position in a maze by following a sequence of natural language commands (
e.g.,
"Right, Up, Up, Right"). Given the input text, the agent (green circle) needs to interpret the commands and take actions based on such interpretation to generate an action sequence (a). The agent receives a reward of
1
if it reaches the goal (red star) and
0
otherwise. Because the agent doesn't have access to any visual information, the only way for the agent to solve this task and generalize to novel instructions is by correctly interpreting the instructions.
In this instruction-following task, the action trajectories a
1
, a
2
and a
3
reach the goal, but the sequences a
2
and a
3
do not follow the instructions. This illustrates the issue of underspecified rewards.
In these tasks, the RL agent needs to learn to generalize from
sparse
(only a few trajectories lead to a non-zero reward) and
underspecified
(no distinction between purposeful and accidental success) rewards. Importantly, because of underspecified rewards, the agent may receive positive feedback for exploiting spurious patterns in the environment. This can lead to
reward hacking
, causing unintended and harmful behavior when deployed in real-world systems.
In "
Learning to Generalize from Sparse and Underspecified Rewards
", we address the issue of
underspecified
rewards by developing
Meta Reward Learning
(
MeRL
), which provides more refined feedback to the agent by optimizing an auxiliary reward function.
MeRL
is combined with a memory buffer of successful trajectories collected using a novel
exploration
strategy to learn from sparse rewards. The effectiveness of our approach is demonstrated on
semantic parsing
, where the goal is to learn a mapping from natural language to
logical forms
(
e.g.,
mapping questions to
SQL
programs). In the paper, we investigate the
weakly-supervised
problem setting, where the goal is to automatically discover logical programs from question-answer pairs, without any form of program supervision. For instance, given the question "
Which nation won the most silver medals?
" and
a relevant Wikipedia table
, an agent needs to generate an SQL-like program that results in the correct answer (
i.e.,
"Nigeria").
The proposed approach achieves state-of-the-art results on the
WikiTableQuestions
and
WikiSQL
benchmarks, improving upon
prior work
by
1.2%
and
2.4%
respectively. MeRL automatically learns the auxiliary reward function without using any expert demonstration
s,
(e.g.,
ground-truth
programs) making it more widely applicable and distinct from
previous
reward
learning
approaches. The diagram below depicts a high level overview of our approach:
Overview of the proposed approach. We employ (1) mode covering exploration to collect a diverse set of successful trajectories in a memory buffer; (2) Meta-learning or Bayesian optimization to learn an auxiliary reward that provides more refined feedback for policy optimization.
Meta Reward Learning (MeRL)
The key insight of MeRL in dealing with underspecified rewards is that spurious trajectories and programs that achieve accidental success are detrimental to the agent's
generalization performance
. For example, an agent might be able to solve a specific instance of the maze problem above. However, if it learns to perform spurious actions during training, it is likely to fail when provided with unseen instructions. To mitigate this issue, MeRL
optimizes a more refined auxiliary reward function, which can differentiate between accidental and purposeful success based on features of action trajectories. The auxiliary reward is optimized by maximizing the trained agent's performance on a hold-out
validation set
via
meta learning
.
Schematic illustration of MeRL: The RL agent is trained via the reward signal obtained from the auxiliary reward model while the auxiliary rewards are trained using the generalization error of the agent.
Learning from Sparse Rewards
To learn from
sparse
rewards, effective exploration is critical to find a set of successful trajectories.
Our paper
addresses this challenge by utilizing
the two directions
of
Kullback–Leibler
(KL) divergence, a measure on how different two probability distributions are. In the example below, we use KL divergence to minimize the difference between a fixed
bimodal
(shaded purple) and a learned gaussian (shaded green) distribution, which can represent the distribution of the agent's optimal policy and our learned policy respectively. One direction of the KL objective learns a distribution which tries to cover both the modes while the distribution learned by other objective seeks a particular mode (i.e. it prefers one mode over another). Our method exploits the
mode covering
KL's tendency to focus on multiple peaks to collect a diverse set of successful trajectories and
mode seeking
KL's implicit preference between trajectories to learn a robust policy.
Left: Optimizing
mode covering
KL. Right: Optimizing
mode seeking
KL
Conclusion
Designing reward functions that distinguish between optimal and suboptimal behavior is critical for applying RL to real-world applications. This research takes a small step in the direction of
modelling reward functions
without any human supervision. In future work, we'd like to tackle the
credit-assignment problem
in RL from the perspective of automatically learning a dense reward function.
Acknowledgements
This research was done in collaboration with Chen Liang and Dale Schuurmans. We thank Chelsea Finn and Kelvin Guu for their review of the paper.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
