  Since the earliest days of reinforcement learning, the workhorse method for
assigning credit to actions over time has been temporal-difference (TD)
learning, which propagates credit backward timestep-by-timestep. This approach
suffers when delays between actions and rewards are long and when intervening
unrelated events contribute variance to long-term returns. We propose
state-associative (SA) learning, where the agent learns associations between
states and arbitrarily distant future rewards, then propagates credit directly
between the two. In this work, we use SA-learning to model the contribution of
past states to the current reward. With this model we can predict each state's
contribution to the far future, a quantity we call "synthetic returns".
TD-learning can then be applied to select actions that maximize these synthetic
returns (SRs). We demonstrate the effectiveness of augmenting agents with SRs
across a range of tasks on which TD-learning alone fails. We show that the
learned SRs are interpretable: they spike for states that occur after critical
actions are taken. Finally, we show that our IMPALA-based SR agent solves Atari
Skiing -- a game with a lengthy reward delay that posed a major hurdle to
deep-RL agents -- 25 times faster than the published state-of-the-art.

Machine Learning