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Title: Observational Overfitting in Reinforcement Learning

Paper content:
A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP).
We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP.
When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting.
Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL).
Generalization for RL has recently grown to be an important topic for agents to perform well in unseen environments.
Complication arises when the dynamics of the environments entangle with the observation, which is often a high-dimensional projection of the true latent state.
One particular framework, which we denote by zero-shot supervised framework (Zhang et al., 2018a; Nichol et al., 2018; Justesen et al., 2018) and is used to study RL generalization, is to treat it analogous to a classical supervised learning (SL) problem -i.e. assume there exists a distribution of MDP's, train jointly on a finite "training set" sampled from this distribution, and check expected performance on the entire distribution, with the fixed trained policy.
In this framework, there is a spectrum of analysis, ranging from almost purely theoretical analysis (Wang et al., 2019; Asadi et al., 2018) to full empirical results on diverse environments Packer et al., 2018) .
However, there is a lack of analysis in the middle of this spectrum.
On the theoretical side, previous work do not provide analysis for the case when the underlying MDP is relatively complex and requires the policy to be a non-linear function approximator such as a neural network.
On the empirical side, there is no common ground between recently proposed empirical benchmarks.
This is partially caused by multiple confounding factors for RL generalization that can be hard to identify and separate.
For instance, an agent can overfit to the MDP dynamics of the training set, such as for control in Mujoco (Pinto et al., 2017; Rajeswaran et al., 2017) .
In other cases, an RNN-based policy can overfit to maze-like tasks in exploration , or even exploit determinism and avoid using observations (Bellemare et al., 2012; Machado et al., 2018) .
Furthermore, various hyperparameters such as the batch-size in SGD (Smith et al., 2018) , choice of optimizer (Kingma & Ba, 2014) , discount factor γ (Jiang et al., 2015) and regularizations such as entropy and weight norms (Cobbe et al., 2018) can also affect generalization.
Due to these confounding factors, it can be unclear what parts of the MDP or policy are actually contributing to overfitting or generalization in a principled manner, especially in empirical studies with newly proposed benchmarks.
In order to isolate these factors, we study one broad factor affecting generalization that is most correlated with themes in SL, specifically observational overfitting, where an agent overfits due to properties of the observation which are irrelevant to the latent dynamics of the MDP family.
To study this factor, we fix a single underlying MDP's dynamics and generate a distribution of MDP's by only modifying the observational outputs.
Our contributions in this paper are the following:
1. We discuss realistic instances where observational overfitting may occur and its difference from other confounding factors, and design a parametric theoretical framework to induce observational overfitting that can be applied to any underlying MDP.
2. We study observational overfitting with linear quadratic regulators (LQR) in a synthetic environment and neural networks such as multi-layer perceptrons (MLPs) and convolutions in classic Gym environments.
A primary novel result we demonstrate for all cases is that implicit regularization occurs in this setting in RL.
We further test the implicit regularization hypothesis on the benchmark CoinRun from using MLPs, even when the underlying MDP dynamics are changing per level.
3. In the Appendix, we expand upon previous experiments by including full training curve and hyperparamters.
We also provide an extensive analysis of the convex one-step LQR case under the observational overfitting regime, showing that under Gaussian initialization of the policy and using gradient descent on the training cost, a generalization gap must necessarily exist.
The structure of this paper is outlined as follows: Section 2 discusses the motivation behind this work and the synthetic construction to abstract certain observation effects.
Section 3 demonstrates numerous experiments using this synthetic construction that suggest implicit regularization is at work.
Finally, Section 3.4 tests the implicit regularization hypothesis on CoinRun, as well as ablates various ImageNet architectures and margin metrics in the Appendix.
We have identified and isolated a key component of overfitting in RL as the particular case of "observational overfitting", which is particularly attractive for studying architectural implicit regularizations.
We have analyzed this setting extensively, by examining 3 main components:
1. The analytical case of LQR and linear policies under exact gradient descent, which lays the foundation for understanding theoretical properties of networks in RL generalization.
2. The empirical but principled Projected-Gym case for both MLP and convolutional networks which demonstrates the effects of neural network policies under nonlinear environments.
3. The large scale case for CoinRun, which can be interpreted as a case where relevant features are moving across the input, where empirically, MLP overparametrization also improves generalization.
We noted that current network policy bounds using ideas from SL are unable to explain overparametrization effects in RL, which is an important further direction.
In some sense, this area of RL generalization is an extension of static SL classification from adding extra RL components.
For instance, adding a nontrivial "combination function" between f and g θ that is dependent on time (to simulate how object pixels move in a real game) is both an RL generalization issue and potentially video classification issue, and extending results to the memory-based RNN case will also be highly beneficial.
Furthermore, it is unclear whether such overparametrization effects would occur in off-policy methods such as Q-learning and also ES-based methods.
In terms of architectural design, recent works (Jacot et al., 2018; Garriga-Alonso et al., 2019; Lee et al., 2019) have shed light on the properties of asymptotically overparametrized neural networks in the infinite width and depth cases and their performance in SL.
Potentially such architectures (and a corresponding training algorithm) may be used in the RL setting which can possibly provide benefits, one of which is generalization as shown in this paper.
We believe that this work provides an important initial step towards solving these future problems.
We further verify that explicit regularization (norm based penalties) also reduces generalization gaps.
However, explicit regularization may be explained due to the bias of the synthetic tasks, since the first layer's matrix may be regularized to only "view" the output of f , especially as regularizing the first layer's weights substantially improves generalization.
Figure A2 : Explicit Regularization on layer norms.
We provide another deconvolution memorization test, using an LQR as the underlying MDP.
While fg-Gym-Deconv shows that memorization performance is dampened, this test shows that there can exist specific hard limits to memorization.
Specifically, NatureCNN can memorize 30 levels, but not 50; IMPALA can memorize 2 levels but not 5; IMPALA-LARGE cannot memorize 2 levels at all.
Training, Test Rewards (f = NULL) IMPALA_2_levels IMPALA_5_levels IMPALA_30_levels IMPALA_LARGE_2_levels NatureCNN_30_levels NatureCNN_50_levels Figure A3 : Deconvolution memorization test using LQR as underlying MDP.

assistant: text: We isolate one factor of RL generalization by analyzing the case when the agent only overfits to the observations. We show that architectural implicit regularizations occur in this regime.