Title: Recall Traces: Backtracking Models for Efficient Reinforcement Learning,
Abstract: In many environments only a tiny subset of all states yield high reward. In
these cases, few of the interactions with the environment provide a relevant
learning signal. Hence, we may want to preferentially train on those
high-reward states and the probable trajectories leading to them. To this end,
we advocate for the use of a backtracking model that predicts the preceding
states that terminate at a given high-reward state. We can train a model which,
starting from a high value state (or one that is estimated to have high value),
predicts and sample for which the (state, action)-tuples may have led to that
high value state. These traces of (state, action) pairs, which we refer to as
Recall Traces, sampled from this backtracking model starting from a high value
state, are informative as they terminate in good states, and hence we can use
these traces to improve a policy. We provide a variational interpretation for
this idea and a practical algorithm in which the backtracking model samples
from an approximate posterior distribution over trajectories which lead to
large rewards. Our method improves the sample efficiency of both on- and
off-policy RL algorithms across several environments and tasks.