Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning

06/01/2018 ∙ by Kavosh Asadi, et al. ∙ 0

Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model in the approximate setting is challenging. Recently Farahmand et al. (2017) proposed a value-aware (VAML) objective that captures the structure of value function during model learning. Using tools from Lipschitz continuity, we show that minimizing the VAML objective is in fact equivalent to minimizing the Wasserstein metric.



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1 Introduction

The model-based approach to reinforcement learning consists of learning an internal model of the environment and planning with the learned model (Sutton & Barto, 1998). The main promise of the model-based approach is data-efficiency: the ability to perform policy improvements with a relatively small number of environmental interactions.

Although the model-based approach is well-understood in the tabular case (Kaelbling et al., 1996; Sutton & Barto, 1998), the extension to approximate setting is difficult. Models usually have non-zero generalization error due to limited training samples. Moreover, the model learning problem can be unrelizable, leading to an imperfect model with irreducible error (Ross & Bagnell, 2012; Talvitie, 2014). Sometimes referred to as the compounding error phenomenon, it has been shown that such small modeling errors can also compound after multiple steps and degrade the policy learned using the model (Talvitie, 2014; Venkatraman et al., 2015; Asadi et al., 2018).

On way of addressing this problem is by learning a model that is tailored to the specific planning algorithm we intend to use. That is, even though the model is imperfect, it is useful for the planning algorithm that is going to leverage it. To this end, Farahmand et al. (2017) proposed an objective function for model-based RL that captures the structure of value function during model learning to ensure that the model is useful for Value Iteration. Learning a model using this loss, known as value-aware model learning (VAML) loss, empirically improved upon a model learned using maximum-likelihood objective, thus providing a promising direction for learning useful models in the approximate setting.

More specifically, VAML minimizes the maximum Bellman error given the learned model, MDP dynamics, and an arbitrary space of value functions. As we will show, computing the Wasserstein metric involves a similar maximization problem, but over a space of Lipschitz functions. Under certain assumptions, we prove that the value function of an MDP is Lipschitz. Therefore, minimizing the VAML objective is in fact equivalent to minimizing Wasserstein.

2 Background

2.1 MDPs

We consider the Markov decision process (MDP) setting in which the RL problem is formulated by the tuple

. Here, denotes a state space and denotes an action set. The functions and denote the reward and transition dynamics. Finally is the discount rate.

2.2 Lipschitz Continuity

We make use of the notion of “smoothness” of a function as quantified below.

Definition 1.

Given two metric spaces and consisting of a space and a distance metric, a function is Lipschitz continuous (sometimes simply Lipschitz) if the Lipschitz constant, defined as


is finite.

Equivalently, for a Lipschitz ,

Note that the input and output of

can generally be scalars, vectors, or probability distributions. A Lipschitz function

is called a non-expansion when and a contraction when . We also define Lipschitz continuity over a subset of inputs:

Definition 2.

A function is uniformly Lipschitz continuous in if


is finite.

Note that the metric is still defined only on . Below we also present two useful lemmas.

Lemma 1.

(Composition Lemma) Define three metric spaces , , and . Define Lipschitz functions and with constants and . Then, is Lipschitz with constant .


Lemma 2.

(Summation Lemma) Define two vector spaces and . Define Lipschitz functions and with constants and . Then, is Lipschitz with constant .


2.3 Distance Between Distributions

We require a notion of difference between two distributions quantified below.

Definition 3.

Given a metric space and the set of all probability measures on , the Wasserstein metric (or the 1st Kantorovic metric) between two probability distributions and in is defined as



denotes the collection of all joint distributions

on with marginals and  (Vaserstein, 1969).

Wasserstein is linked to Lipschitz continuity using duality:


This equivalence is known as Kantorovich-Rubinstein duality (Kantorovich & Rubinstein, 1958; Villani, 2008). Sometimes referred to as “Earth Mover’s distance”, Wasserstein has recently become popular in machine learning, namely in the context of generative adversarial networks (Arjovsky et al., 2017) and value distributions in reinforcement learning (Bellemare et al., 2017)

. We also define Kullback Leibler divergence (simply KL) as an alternative measure of difference between two distributions:

3 Value-Aware Model Learning (VAML) Loss

The basic idea behind VAML (Farahmand et al., 2017) is to learn a model tailored to the planning algorithm that intends to use it. Since Bellman equations (Bellman, 1957) are in the core of many RL algorithms (Sutton & Barto, 1998), we assume that the planner uses the following Bellman equation:

where can generally be any arbitrary operator (Littman & Szepesvári, 1996) such as max. We also define:

A good model could then be thought of as the one that minimizes the error:

Note that minimizing this objective requires access to the value function in the first place, but we can obviate this need by leveraging Holder’s inequality:

Further, we can use Pinsker’s inequality to write:

This justifies the use of maximum likelihood estimation for model learning, a common practice in model-based RL

(Bagnell & Schneider, 2001; Abbeel et al., 2006; Agostini & Celaya, 2010), since maximum likelihood estimation is equivalent to empirical KL minimization.

However, there exists a major drawback with the KL objective, namely that it ignores the structure of the value function during model learning. As a simple example, if the value function is constant through the state-space, any randomly chosen model will, in fact, yield zero Bellman error. However, a model learning algorithm that ignores the structure of value function can potentially require many samples to provide any guarantee about the performance of learned policy.

Consider the objective function , and notice again that itself is not known so we cannot directly optimize for this objective. Farahmand et al. (2017) proposed to search for a model that results in lowest error given all possible value functions belonging to a specific class:


Note that minimizing this objective is shown to be tractable if, for example, is restricted to the class of exponential functions. Observe that the VAML objective (5) is similar to the dual of Wasserstein (4), but the main difference is the space of value functions. In the next section we show that even the space of value functions are the same under certain conditions.

4 Lipschitz Generalized Value Iteration

We show that solving for a class of Bellman equations yields a Lipschitz value function. Our proof is in the context of GVI (Littman & Szepesvári, 1996), which defines Value Iteration (Bellman, 1957) with arbitrary backup operators. We make use of the following lemmas.

Lemma 3.

Given a non-expansion :


Starting from the definition, we write:

Lemma 4.

The following operators are non-expansion ():

  1. -


1 is proven by Littman & Szepesvári (1996). 2 follows from 1: (metrics not shown for brevity)

Finally, 3 is proven multiple times in the literature. (Asadi & Littman, 2017; Nachum et al., 2017; Neu et al., 2017)

  Input: initial , , and choose an operator
     for each  do
        for each  do
        end for
     end for
Algorithm 1 GVI algorithm

We now present the main result of this paper.

For any choice of backup operator outlined in Lemma 4, GVI computes a value function with a Lipschitz constant bounded by if .


From Algorithm 1, in the th round of GVI updates we have:

First observe that:

(due to Summation Lemma (2))
(due to Lemma (3))
(due to Composition Lemma (1))
(due to Lemma (4), the non-expansion property of )


By computing the limit of both sides, we get:

where we used the fact that

This concludes the proof.∎

Now notice that as defined earlier:

so as a relevant corollary of our theorem we get:

That is, solving for the fixed point of this general class of Bellman equations results in a Lipschitz state-value function.

5 Equivalence Between VAML and Wasserstein

We now show the main claim of the paper, namely that minimzing for the VAML objective is the same as minimizing the Wasserstein metric.

Consider again the VAML objective:

where can generally be any class of functions. From our theorem, however, the space of value functions should be restricted to Lipschitz functions. Moreover, it is easy to design an MDP and a policy such that a desired Lipschitz value function is attained.

This space can then be defined as follows:


So we can rewrite the VAML objective as follows:

It is clear that a function that maximizes the Kantorovich-Rubinstein dual form:

will also maximize:

This is due to the fact that and so computing absolute value or squaring the term will not change in this case.

As a result:

This highlights a nice property of Wasserstein, namely that minimizing this metric yields a value-aware model.

6 Conclusion and Future Work

We showed that the value function of an MDP is Lipschitz. This result enabled us to draw a connection between value-aware model-based reinforcement learning and the Wassertein metric.

We hypothesize that the value function is Lipschitz in a more general sense, and so, further investigation of Lipschitz continuity of value functions should be interesting on its own. The second interesting direction relates to design of practical model-learning algorithms that can minimize Wasserstein. Two promising directions are the use of generative adversarial networks (Goodfellow et al., 2014; Arjovsky et al., 2017) or approximations such as entropic regularization (Frogner et al., 2015). We leave these two directions for future work.


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