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Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy.
Yunshu Du, Garrett
Warnell, Assefaw Gebremedhin, Peter Stone, and Matthew
E. Taylor.
Neural Computing and Applications, May 2021.
Experience replay (ER) improves the data efficiency of off-policy reinforcement learning (RL) algorithms by allowing an agent to store and reuse its past experiences in a replay buffer. While many techniques have been proposed to enhance ER by biasing how experiences are sampled from the buffer, thus far they have not considered strategies for refreshing experiences inside the buffer. In this work, we introduce Lucid Dreaming for Experience Replay (LiDER), a conceptually new framework that allows replay experiences to be refreshed by leveraging the agent's current policy. LiDER consists of three steps: First, LiDER moves an agent back to a past state. Second, from that state, LiDER then lets the agent execute a sequence of actions by following its current policy---as if the agent were ``dreaming'' about the past and can try out different behaviors to encounter new experiences in the dream. Third, LiDER stores and reuses the new experience if it turned out better than what the agent previously experienced, i.e., to refresh its memories. LiDER is designed to be easily incorporated into off-policy, multi-worker RL algorithms that use ER; we present in this work a case study of applying LiDER to an actor--critic-based algorithm. Results show LiDER consistently improves performance over the baseline in six Atari 2600 games. Our open-source implementation of LiDER and the data used to generate all plots in this work are available at https://github.com/duyunshu/lucid-dreaming-for-exp-replay.
@article{NCAA21-Du,
author={Yunshu Du and Garrett Warnell and Assefaw Gebremedhin and Peter Stone and Matthew E. Taylor},
title={Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy},
journal={Neural Computing and Applications},
year={2021},
month={May},
day={25},
abstract={
Experience replay (ER) improves the data efficiency of
off-policy reinforcement learning (RL) algorithms by
allowing an agent to store and reuse its past experiences in
a replay buffer. While many techniques have been proposed to
enhance ER by biasing how experiences are sampled from the
buffer, thus far they have not considered strategies for
refreshing experiences inside the buffer. In this work, we
introduce Lucid Dreaming for Experience Replay (LiDER), a
conceptually new framework that allows replay experiences to
be refreshed by leveraging the agent's current policy. LiDER
consists of three steps: First, LiDER moves an agent back to
a past state. Second, from that state, LiDER then lets the
agent execute a sequence of actions by following its current
policy---as if the agent were ``dreaming'' about the past
and can try out different behaviors to encounter new
experiences in the dream. Third, LiDER stores and reuses the
new experience if it turned out better than what the agent
previously experienced, i.e., to refresh its memories. LiDER
is designed to be easily incorporated into off-policy,
multi-worker RL algorithms that use ER; we present in this
work a case study of applying LiDER to an
actor--critic-based algorithm. Results show LiDER
consistently improves performance over the baseline in six
Atari 2600 games. Our open-source implementation of LiDER
and the data used to generate all plots in this work are
available at
https://github.com/duyunshu/lucid-dreaming-for-exp-replay.},
issn={1433-3058},
doi={10.1007/s00521-021-06104-5},
url={https://doi.org/10.1007/s00521-021-06104-5},
}
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