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Title: Recurrent Experience Replay in Distributed Reinforcement Learning

Paper content:
Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay.
We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy.
Using a single network architecture and fixed set of hyper-parameters, the resulting agent, Recurrent Replay Distributed DQN, quadruples the previous state of the art on Atari-57, and matches the state of the art on DMLab-30.
It is the first agent to exceed human-level performance in 52 of the 57 Atari games.
Reinforcement Learning (RL) has seen a rejuvenation of research interest recently due to repeated successes in solving challenging problems such as reaching human-level play on Atari 2600 games BID15 , beating the world champion in the game of Go BID21 , and playing competitive 5-player DOTA BID18 .
The earliest of these successes leveraged experience replay for data efficiency and stacked a fixed number of consecutive frames to overcome the partial observability in Atari 2600 games.
However, with progress towards increasingly difficult, partially observable domains, the need for more advanced memory-based representations increases, necessitating more principled solutions such as recurrent neural networks (RNNs).
The use of LSTMs BID8 within RL has been widely adopted to overcome partial observability BID5 BID16 BID3 BID4 .In
this paper we investigate the training of RNNs with experience replay. We
have three primary contributions. First
, we demonstrate the effect of experience replay on parameter lag, leading to representational drift and recurrent state staleness. This
is potentially exacerbated in the distributed training setting, and ultimately results in diminished training stability and performance. Second
, we perform an empirical study into the effects of several approaches to RNN training with experience replay, mitigating the aforementioned effects. Third
, we present an agent that integrates these findings to achieve significant advances in the state of the art on Atari-57 BID1 and matches the state of the art on DMLab-30 BID0 . To the
best of our knowledge, our agent, Recurrent Replay Distributed DQN (R2D2), is the first to achieve this using a single network architecture and fixed set of hyper-parameters.
Here we take a step back from evaluating performance and discuss our empirical findings in a broader context.
There are two surprising findings in our results.First, although zero state initialization was often used in previous works BID5 BID4 , we have found that it leads to misestimated action-values, especially in the early states of replayed sequences.
Moreover, without burn-in, updates through BPTT to these early time steps with poorly estimated outputs seem to give rise to destructive updates and hinder the network's ability to recover from sub-optimal initial recurrent states.
This suggests that either the context-dependent recurrent state should be stored along with the trajectory in replay, or an initial part of replayed sequences should be reserved for burn-in, to allow the RNN to rely on its recurrent state and exploit long-term temporal dependencies, and the two techniques can also be combined beneficially.
We have also observed that the underlying problems of representational drift and recurrent state staleness are potentially exacerbated in the distributed setting (see Appendix), highlighting the importance of robustness to these effects through an adequate training strategy of the RNN.Second, we found that the impact of RNN training goes beyond providing the agent with memory.
Instead, RNN training also serves a role not previously studied in RL, potentially by enabling better representation learning, and thereby improves performance even on domains that are fully observable and do not obviously require memory (cf. BREAKOUT results in the feed-forward ablation).Finally
, taking a broader view on our empirical results, we note that scaling up of RL agents through parallelization and distributed training allows them to benefit from huge experience throughput and achieve ever-increasing results over broad simulated task suites such as Atari-57 and DMLab-30. Impressive
as these results are in terms of raw performance, they come at the price of high sample complexity, consuming billions of simulated time steps in hours or days of wall-clock time. One widely
open avenue for future work lies in improving the sample efficiency of these agents, to allow applications to domains that do not easily allow fast simulation at similar scales. Another remaining
challenge, very apparent in our results on Atari-57, is exploration: Save for the hardest-exploration games from Atari-57, R2D2 surpasses human-level performance on this task suite significantly, essentially 'solving' many of the games therein. Figure 6 : Left:
Parameter lag experienced with distributed prioritized replay with (top) 256 and (bottom) 64 actors on four DMLab levels: explore obstructed goals large (eogl), explore object rewards many (eorm), lasertag three opponents small (lots), rooms watermaze (rw). Center: initialstate
and Right: final-state Q-value discrepancy for the same set of experiments.

assistant: text: Investigation on combining recurrent neural networks and experience replay leading to state-of-the-art agent on both Atari-57 and DMLab-30 using single set of hyper-parameters.