LEARNING TO UNDERSTAND GOAL SPECIFICATIONS BY MODELLING REWARD

LEARNING TO UNDERSTAND GOAL SPECIFICATIONS
BY MODELLING REWARD
Dzmitry Bahdanau∗
MILA
University of Montreal
Montreal, Canada
[email protected]
Felix Hill
DeepMind
[email protected]
Jan Leike
DeepMind
[email protected]
Edward Hughes
DeepMind
[email protected]
Pushmeet Kohli
DeepMind
[email protected]
Edward Grefenstette
DeepMind
[email protected]
ABSTRACT
Recent work has shown that deep reinforcement-learning agents can learn to follow
language-like instructions from infrequent environment rewards. However, this
places on environment designers the onus of designing language-conditional reward
functions which may not be easily or tractably implemented as the complexity of
the environment and the language scales. To overcome this limitation, we present
a framework within which instruction-conditional RL agents are trained using
rewards obtained not from the environment, but from reward models which are
jointly trained from expert examples. As reward models improve, they learn to
accurately reward agents for completing tasks for environment configurations—and
for instructions—not present amongst the expert data. This framework effectively
separates the representation of what instructions require from how they can be
executed. In a simple grid world, it enables an agent to learn a range of commands
requiring interaction with blocks and understanding of spatial relations and underspecified
abstract arrangements. We further show the method allows our agent to
adapt to changes in the environment without requiring new expert examples.