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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.
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