Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning

We study risk-sensitive reinforcement learning (RL) based on the entropic risk measure. Although existing works have established non-asymptotic regret guarantees for this problem, they leave open an exponential gap between the upper and lower bounds. We identify the deficiencies in existing algorithms and their analysis that result in such a gap. To remedy these deficiencies, we investigate a simple transformation of the risk-sensitive Bellman equations, which we call the exponential Bellman equation. The exponential Bellman equation inspires us to develop a novel analysis of Bellman backup procedures in risk-sensitive RL algorithms, and further motivates the design of a novel exploration mechanism. We show that these analytic and algorithmic innovations together lead to improved regret upper bounds over existing ones.



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1 Introduction

Risk-sensitive reinforcement learning (RL) is important for practical and high-stake applications, such as self-driving and robotic surgery. In contrast with standard and risk-neutral RL, it optimizes some risk measure of cumulative rewards instead of their expectation. One foundational framework for risk-sensitive RL maximizes the entropic risk measure of the reward, which takes the form of

with respect to the policy , where is a given risk parameter and denotes the cumulative rewards.

Recently, the works of [20, 21] investigate the online setting of the above risk-sensitive RL problem. Under -episode MDPs with horizon length of , they propose two model-free algorithms, namely RSVI and RSQ, and prove that their algorithms achieve the regret upper bound (with its informal form given by)

without assuming knowledge of the transition distribution or access to a simulator. They also provide a lower bound (informally presented as)

that any algorithm has to incur, where is a linear function of . Despite the non-asymptotic nature of their results, it is not hard to see that a wide gap exists between the two bounds. Specifically, the upper bound has an additional factor compared to the lower bound, and even worse, this factor is dominating in the upper bound since the quadratic exponent in makes it exponentially larger than even for moderate values of and . It is unclear whether the factor of is intrinsic in the upper bound.

In this paper, we show that the additional factor in the upper bound is not intrinsic for the upper bound and can be eliminated by a refined algorithmic design and analysis. We identify two deficiencies in the existing algorithms and their analysis: (1) the main element of the analysis follows existing analysis of risk-neutral RL algorithms, which fails to exploit the special structure of the Bellman equations of risk-sensitive RL; (2) the existing algorithms use an excessively large bonus that results in the exponential blow-up in the regret upper bound.

To address the above shortcomings, we consider a simple transformation of the Bellman equations analyzed so far in the literature, which we call the exponential Bellman equation. A distinctive feature of the exponential Bellman equation is that they associate the instantaneous reward and value function of the next step in a multiplicative way, rather than in an additive way as in the standard Bellman equations. From the exponential Bellman equation, we develop a novel analysis of the Bellman backup procedure for risk-sensitive RL algorithms that are based on the principle of optimism. The analysis further motivates a novel exploration mechanism called doubly decaying

bonus, which helps the algorithms adapt to their estimation error over each horizon step while at the same time exploring efficiently. These discoveries enable us to propose two model-free algorithms for RL with the entropic risk measure based on the novel bonus. By combining the new analysis and bonus design, we prove that the preceding algorithms attain nearly optimal regret bounds under episodic and finite-horizon MDPs. Compared to prior results, our regret bounds feature an exponential improvement with respect to the horizon length and risk parameter, removing the factor of

from existing upper bounds. This significantly narrows the gap between upper bounds and the existing lower bound of regret.

In summary, we make the following theoretical contributions in this paper.

  1. We investigate the gap between existing upper and lower regret bounds in the context of risk-sensitive RL, and identify deficiencies of the existing algorithms and analysis;

  2. We consider the exponential Bellman equation, which inspires us to propose a novel analysis of the Bellman backup procedure for RL algorithms based on the entropic risk measure. It further motivates a novel bonus design called doubly decaying bonus. We then design two model-free risk-sensitive RL algorithms equipped with the novel bonus.

  3. The novel analytic framework and bonus design together enable us to prove that the preceding algorithms achieve nearly optimal regret bounds, which improve upon existing ones by an exponential factor in terms of the horizon length and risk sensitivity.

2 Related works

The problem of RL with respect to the entropic risk measure is first proposed by the classical work of [24], and has since inspired a large body of studies [2, 4, 5, 6, 7, 8, 13, 16, 17, 18, 22, 23, 25, 26, 31, 33, 37, 38, 40, 41, 43]. However, the algorithms from this line of works require knowledge of the transition kernel or assume access to a simulator of the underlying environment. Theoretical properties of these algorithms are investigated based on these assumptions, but the results are mostly of asymptotic nature, which do not shed light on their dependency on key parameters of the environment and agent.

The work of [20] represents the first effort to investigate the setting where transitions are unknown and simulators of the environment are unavailable. It establishes the first non-asymptotic regret or sample complexity guarantees under the tabular setting. Building upon [20], the authors of [21] extend the results to the function approximation setting, by considering linear and general function approximations of the underlying MDPs. Nevertheless, as discussed in Section 1, both works leave open an exponential gap between the regret upper and lower bounds, which the present work aims to address via novel algorithms and analysis motivated by the exponential Bellman equation.

We remark that although the exponential Bellman equation has been previously investigated in the literature of risk-sensitive RL [5, 2], this is the first time that it is explored for deriving regret and sample complexity guarantees of risk-sensitive RL algorithms. In Appendix A, we also make connections between risk-sensitive RL and distributional RL through the exponential Bellman equation.


For a positive integer , we let . For two non-negative sequences and , we write if there exists a universal constant such that for all , and write if and . We use to denote while hiding logarithmic factors. For functions , where denotes their domain, we write if for any . We denote by the indicator function.

3 Problem background

3.1 Episodic and finite-horizon MDP

The setting of episodic Markov decision processes can be denoted by

, where is the set of states, is the set of actions, is the length of each episode, and and are the sets of transition kernels and reward functions, respectively. We let and , and we assume . We let

denote the probability distribution over successor states of step

if action is executed in state at step . We assume that the reward function is deterministic. We also assume that both and are unknown to learning agents.

Under the setting of an episodic MDP, the agent aims to learn the optimal policy by interacting with the environment throughout episodes, described as follows. At the beginning of episode , an initial state is selected by the environment and we assume stays the same for all . In each step of episode , the agent observes state , executes an action , and receives a reward equal to from the environment. The MDP then transitions into state randomly drawn from the transition kernel . The episode terminates at step , in which the agent does not take actions or receive rewards. We define a policy as a collection of functions , where is the action that the agent takes in state at step of the episode.

3.2 Risk-sensitive RL

For each , we define the value function of a policy as the cumulative utility of the agent at state of step under the entropic risk measure, assuming that the agent commits to policy in later steps. Specifically, we define


where is a given risk parameter. The agent aims to maximize his cumulative utility in step 1, that is, to find a policy such that is maximized for all state . Under this setting, if , the agent is risk-seeking and if , the agent is risk-averse. Furthermore, as the agent tends to be risk-neutral and tends to the classical value function.

We may also define the action-value function , which is the cumulative utility of the agent who follows policy , conditional on a particular state-action pair; formally, this is given by


Under some mild regularity conditions [2], there always exists an optimal policy, which we denote as , that yields the optimal value for all .

Bellman equations.

For all , the Bellman equation associated with a policy is given by


for . In Equation (3), it can be seen that the action value of step is a non-linear function of the value function of the later step. This is in contrast with the linear Bellman equations in the risk-neutral setting (), where . Based on Equation (3), for , the Bellman optimality equation is given by


Exponential Bellman equation.

We introduce the exponential Bellman equation, which is an exponential transformation of Equations (3) and (4) (by taking exponential on both sides): for any policy and tuple , we have


When , we obtain the corresponding optimality equation


Note that Equation (5) associates the current and future cumulative utilities ( and ) in a multiplicative way. An implication of Equation (5) is that one may estimate by a quantity of the form


given some estimate of the value function . Here, we denote by the sample average computed over elements in the set throughout past episodes, and it can be seen as an empirical MGF of cumulative rewards from step . Equation (5) also suggests the following policy improvement procedure for a risk-sensitive policy :


where denotes some estimated action-value function, possibly obtained from the quantity .

In the next section, we will discuss how the exponential Bellman equation (5) inspires the development of a novel analytic framework for risk-sensitive RL. Before proceeding, we introduce a performance metric for the agent. For each episode , recall that is the initial state chosen by the environment and let be the policy of the agent at the beginning of episode . Then the difference is called the regret of the agent in episode . Therefore, after episodes, the total regret for the agent is given by


which serves as the key performance metric studied in this paper.

4 Analysis of risk-sensitive RL

4.1 Mechanism of existing analysis

In this section, we provide an informal overview of the mechanism underlying the existing analysis of risk-sensitive RL. Let us focus on the case for simplicity of exposition; similar reasoning holds for . A key step in the existing regret analysis of RL algorithms is to establish a recursion on the difference over , where is the iterate of an algorithm in step of episode and is the value function of the policy used in episode . Such approach can be commonly found in the literature of algorithms that use the upper confidence bound [27, 28], in which the recursion takes the form of


for and some quantity . The work of [20], which studies the risk-sensitive setting under the entropic risk measure, also follows this approach and derives regret bounds by establishing the recursion of the form


where denotes the bonus which enforces the upper confidence bound and leads to the inequality for any policy , and is part of a martingale difference sequence. The derivation of Equation (11) is based on the Bellman equation (3), which shows that the action value is the sum of the reward and the entropic risk measure of . Following [20], we may then unroll the recursion (11) from to to get


given that . Using the inequality , and , we obtain the regret bound in [20] as . Therefore, it can be seen that the dominating factor in their regret bound originates in Equation (12), which can be further traced back to the exponential factor in the error dynamics (11).

4.2 Refined approach via exponential Bellman equation

While the existing analysis in (11) is motivated by the Bellman equation of the form given in (3), we propose to work on the exponential Bellman equation (5). Equation (5) operates on the quantities and , which can be thought of as the MGFs of the current and future values, while the reward function is involved as a multiplicative term. This motivates us to derive a new recursion:


where denote some bonus and martingale terms, respectively, and stands for the reward in step of episode . Unrolling Equation (13) yields


where . In words, the error of is bounded by the weighted sum of bonus and martingale difference terms, where the weights are given by , the exponential rewards up to step . We may then apply a localized linearization of the logarithmic function, which gives , and arrives at a regret upper bound (the formal regret bounds will be established in Theorems 1 and 2 below). Different from Equation (11) where rewards are only implicitly encoded in , in Equation (13) rewards are explicitly involved in the error dynamics via an exponential term.

To see why Equation (13) is intuitively correct, we may divide both sides of the equation by and take . By doing so, we should expect to obtain quantities from the error dynamics (10) of risk-neutral RL. Since the function satisfies that as for any fixed , we have

recovering terms in (10). Therefore, the recursion (13) can be seen as generalizing those in the analysis of risk-neutral RL.

By comparing Equations (13) and (11), we see that while both error dynamics are derived from the same underlying Bellman equation, they inspire drastically different forms of recursion. Note that the multiplicative factor in Equation (13) is milder than the factor in Equation (11), since . This is the source of an improvement of our refined analysis over existing works. On the other hand, the success of applying the error dynamics (13) in our analysis crucially depends on the choice of bonus terms , as an improper choice would blow up the error . This observation motivates our novel bonus design, as we explain next in Section 5.

5 Algorithms

5.1 Overview of algorithms

In this section, we propose two model-free algorithms for RL with the entropic risk measure. We first present RSVI2, which is based on value iteration, in Algorithm 1. The algorithm has two main stages: it first estimates the value function using data accumulated up to episode (Line 310) and then executes the estimated policy to collect new trajectory (Line 11). In value function estimation, it computes the weights , or the empirical MGF of some estimated cumulative rewards evaluated at , which can be seen as a simple moving average over . Therefore, Line 5 functions as a concrete implementation of Equation (7) where the sample average is instantiated as a simple moving average. Then in Line 7, it computes an augmented estimate by combining with a bonus term (defined in Line 6). This is followed by thresholding to put in the proper range. Note that is an optimistic estimator of the quantity in Equation (5): the construction of is augmented by so that it encourages exploration of rarely visited state-action pairs in future episodes, and thereby follows the principle of Risk-Sensitive Optimism in the Face of Uncertainty [20]. When , the bonus is subtracted from , since a higher level of optimism corresponds to a smaller value of the estimate. In addition, Line 11 follows the reasoning of policy improvement suggested in Equation (8).

1:, and for all
2:for episode  do
3:     for step  do
4:         for  such that  do
6:               where is a universal constant
9:         end for
10:     end for
11:     , take ; observe ,
12:     Add 1 to
13:end for
Algorithm 1 RSVI2

Next, we introduce RSQ2 in Algorithm 2, which is based on Q-learning. Similar to Algorithm 1, it consists of value estimation (Line 811) and policy execution (Line 6) steps. By combining Lines 9 and 10, we see that Algorithm 2 computes the optimistic estimate as a projection of an exponential moving average of empirical MGFs:


where denotes a projection that depends on step . In particular, Line 9 can be interpreted as a computation of empirical MGFs evaluated at and thus a concrete implementation of Equation (7) using an exponential moving average. This is in contrast with the simple moving average update in Algorithm 1.

1: otherwise, for all
2: for all ; for
3:for episode  do
4:     Receive the initial state
5:     for step  do
6:         Take action and observe and
7:         Add 1 to ;
8:          for some universal constant
12:     end for
13:end for
Algorithm 2 RSQ2

Although Algorithms 1 and 2 are inspired by RSVI and RSQ of [20], respectively, we note that the main novelty of our algorithms lies in the bonus terms ( in Algorithm 1 and in Algorithm 2), which we call the doubly decaying bonus. We discuss this new bonus design in the following.

5.2 Doubly decaying bonus

Let us focus on for this discussion. In optimism-based algorithms, the bonus term is used to enforce the upper confidence bound in order to encourage sufficient exploration in uncertain environments. It takes the form of a multiplier times a factor that is inversely proportional to visit counts . Our bonus follows this structure and is given by


ignoring factors that do not vary in . In Equation (16), the quantity plays the role of the multiplier and is the factor that decreases in the visit count. While the component is common in bonus terms, our new bonus is designed to shrink its multiplier deterministically and exponentially across the horizon steps, as decreases from in step to in step . This is in sharp contrast with the bonus terms typically found in risk-neutral RL algorithms, where the multipliers are kept constant in (usually as a constant multiple of ). Furthermore, our bonus design is also in contrast with that in RSVI and RSQ proposed by [20], whose multiplier is and kept fixed along the horizon. Because decays both in the visit count (across episodes) and the multiplier (across the horizon), we name it as doubly decaying bonus. We remark that this is a novel feature of Algorithms 1 and 2, compared to RSVI and RSQ. Let us discuss how this new exploration mechanism is motivated from the error dynamics (14).

Motivation of exponential decay.

From Equation (14), we see that the error of the iterate is bounded by the sum of weighted bonus terms, where the weights are of the form and . Choosing ensures that the weighted bonus is on the order of at maximum. On the other hand, if we use the bonus as in [20], which is proportional to , then we would end up with a multiplicative factor in regret, which is exponentially larger than . An alternative way to understanding the exponential decay of our bonus is as follows. At step , the estimated value function is , which implies . The iterate (of Algorithm 1 or 2) is used to estimate , with its estimation error given by

where denotes an empirical average operator over historical data in step . Therefore, the estimation error of shrinks exponentially across the horizon. Since bonus is used to compensate for and dominate the estimation error, the minimal order of required is thus , which is exactly the multiplier in Equation (16).

As a passing note, we remark that the decaying multiplier is not necessary in risk-neutral RL algorithms, since the estimation error therein satisfies , which is upper bounded by for all . This implies that it suffices to simply set the bonus multiplier as a constant multiple of . In contrast, as we have explained, the estimation error of our algorithms decays exponentially in step , and an adaptive and exponentially decaying bonus is needed.

Comparison with Bernstein-type bonus.

We also compare our bonus in Equation (16) with the Bernstein-type bonus commonly used to improve sample efficiency of risk-neutral RL algorithms [1, 27]. The Bernstein-type bonus takes the form of



denotes an empirical variance operator over historical data and

denotes a vanishing term as . Our bonus in Equation (16) is different from the Bernstein-type bonus in Equation (17) in mechanism: our bonus features the multiplier which decays exponentially and deterministically over , whereas the Bernstein-type bonus uses as the multiplier (ignoring the vanishing term). The term depends on the trajectory of the learning process. Therefore the multiplier is stochastic and stays on the polynomial order of across the horizon. Moreover, it is unclear how the multiplier behaves in terms of step .

6 Main results

In this section, we present and discuss our main theoretical results for Algorithms 1 and 2.

Theorem 1.

For any

, with probability at least

there exists a universal constant (used in Algorithm 1), such that the regret of Algorithm 1 is bounded by

Theorem 2.

For any , with probability at least and when is sufficiently large, there exists a universal constant (used in Algorithm 2) such that the regret of Algorithm 2 obeys

The proof of the two theorems are provided in Appendices B and C, respectively. Note that the above results generalize those in the literature of risk-neutral RL: when , we recover the same regret bounds of LSVI in [28] and Q-learning in [27].

Let us discuss the connections between our results and those in [20]. The work of [20] proposes two algorithms, RSVI and RSQ, that attain the regret bound


and a lower bound incurred by any algorithm


where is a linear function in ; for simplicity of presentation, we exclude polynomial dependencies on other parameters and logarithmic factors from the two bounds. In particular, the proof of the lower bound is based on reducing an hard instance of MDP to a multi-armed bandit. It is a priori unclear whether the extra exponential factor in the upper bound (18) is fundamental in the MDP setting, or is due to suboptimal analysis or algorithmic design. We would like to mention that although one trivial way of avoiding the factor in the upper bound (18) is to use a sufficiently small in the algorithms of [20] (e.g., so that ), such a small defeats the very purpose of have an appropriate degree of risk-sensitivity in the algorithms. Hence, an answer for all would be desirable.

In view of Theorems 1 and 2, we see that our Algorithms 1 and 2 achieve regret bounds that are exponentially sharper than those of RSVI and RSQ. In particular, our results eliminate the factor from Equation (18) thanks to the novel analysis and doubly decaying bonus in our algorithms, which are inspired by the exponential Bellman equation (5). As a result, our bounds significantly narrow the gap between upper bounds and the lower bound (19).


Z.Yang acknowledges Simons Institute (Theory of Reinforcement Learning). Y. Chen is partially supported by NSF grant CCF-1704828 and CAREER Award CCF-2047910. Z.Wang acknowledges National Science Foundation (Awards 2048075, 2008827, 2015568, 1934931), Simons Institute (Theory of Reinforcement Learning), Amazon, J.P. Morgan, and Two Sigma for their supports.


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Appendix A Connections to distribution RL

In this appendix, we establish connections between risk-sensitive RL and distributional RL via the lens of the exponential Bellman equation.

Distributional RL has been studied in the line of works [3, 15, 32, 34, 35, 39, 19, 45]. The framework of distributional RL is built upon the following key equation, namely the distributional Bellman equation:


for a fixed policy , where , , and is the reward distribution in step . Here, we use to denote equality in distribution. It can be seen that is the distribution of cumulative rewards under policy at step , when the state and action are visited in step . Based on Equation (20), a distributional Bellman optimality operator is given by


where again . Note that in Equation (21), the optimal action is greedy with respect to the expectation of the distribution . Most existing distributional RL algorithms work with distribution estimates such as quantiles [15, 14] or empirical distribution functions [3, 39].

Now recall the exponential Bellman equation (5), which takes the form


for any fixed , where , and is the deterministic reward function by our assumption. Given the definitions (1) and (2) with replaced by , we note that both and in the above equation depend on the value of (which we omit for simplicity of notations). Then by the definition of in Equation (2), one sees that represents the MGF of the cumulative rewards at step when policy is executed. Hence, the exponential Bellman equation for risk-sensitive RL provides an instantiation of Equation (20) through the MGF of rewards.

Appendix B Proof of Theorem 1

First, we set some notations and definitions. Define for a given . We adopt the shorthands and for . We let be the visit count of at the beginning of episode . We denote by , , the values of , , after the updates in step of episode , respectively. We also set .

For the time being we consider . For , we define

where for any and . It can be seen that in Algorithm 1 can be equivalently defined as


where is the universal constant from Lemma 2. For any , we have