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Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning

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arxiv 2006.05826 v4 pith:FWGOEETR submitted 2020-06-10 cs.LG cs.AIstat.ML

Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning

Maximilian Igl , Gregory Farquhar , Jelena Luketina , Wendelin Boehmer , Shimon Whiteson This is my paper
classification cs.LG cs.AIstat.ML
keywords generalisationnon-stationaritydeepitertraininglearningnetworkneural
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL and a single neural network is continually updated. However, we find evidence that neural networks exhibit a memory effect where these transient non-stationarities can permanently impact the latent representation and adversely affect generalisation performance. Consequently, to improve generalisation of deep RL agents, we propose Iterated Relearning (ITER). ITER augments standard RL training by repeated knowledge transfer of the current policy into a freshly initialised network, which thereby experiences less non-stationarity during training. Experimentally, we show that ITER improves performance on the challenging generalisation benchmarks ProcGen and Multiroom.

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Cited by 3 Pith papers

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    ABS uses Behavioral Divergence to adaptively scale batch sizes in RL according to policy volatility, enabling effective large-batch large-network training on ALE benchmarks.

  2. Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning

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    Utility-scaled partial neuron resets prevent policy collapse in long-horizon continual RL while matching or beating binary-reset and uniform-decay baselines on several benchmarks.

  3. Scalable Reinforcement Learning via Adaptive Batch Scaling

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    Adaptive Batch Scaling dynamically increases batch size in on-policy RL as policy volatility drops, measured by a new Behavioral Divergence metric, and shows larger networks plus larger batches outperform on ALE with PQN.