Neural Reward Machines
Abstract
Non-markovian Reinforcement Learning (RL) tasks are
very hard to solve, because agents must consider the entire history of
state-action pairs to act rationally in the environment. Most works use
symbolic formalisms (as Linear Temporal Logic or automata) to specify the temporally-extended task. These approaches only work in finite
and discrete state environments or continuous problems for which a
mapping between the raw state and a symbolic interpretation is known
as a symbol grounding (SG) function. Here, we define Neural Reward Machines (NRM), an automata-based neurosymbolic framework
that can be used for both reasoning and learning in non-symbolic
non-markovian RL domains, which is based on the probabilistic relaxation of Moore Machines. We combine RL with semisupervised
symbol grounding (SSSG) and we show that NRMs can exploit highlevel symbolic knowledge in non-symbolic environments without
any knowledge of the SG function, outperforming Deep RL methods
which cannot incorporate prior knowledge. Moreover, we advance the
research in SSSG, proposing an algorithm for analysing the groundability of temporal specifications, which is more efficient than baseline
techniques of a factor 103.
Venue
ECAI-24
Date
2025