Previously we derived a regret bound for DRL which assumed the advisor is "locally sane." Such an advisor can only take actions that don't lose any value in the long term. In particular, if the environment contains a latent catastrophe that manifests with a certain rate (such as the possibility of an UFAI), a locally sane advisor has to take the optimal course of action to mitigate it, since every delay yields a positive probability of the catastrophe manifesting and leading to permanent loss of value. This state of affairs is unsatisfactory, since we would like to have performance guarantees for an AI that can mitigate catastrophes that the human operator cannot mitigate on their own. To address this problem, we introduce a new form of DRL where in every hypothetical environment the set of uncorrupted states is divided into "dangerous" (impending catastrophe) and "safe" (catastrophe was mitigated). The advisor is then only required to be locally sane in safe states, whereas in dangerous states certain "leaking" of long-term value is allowed. We derive a regret bound in this setting as a function of the time discount factor, the expected value of catastrophe mitigation time for the optimal policy, and the "value leak" rate (i.e. essentially the rate of catastrophe occurrence). The form of this regret bound implies that in certain asymptotic regimes, the agent attains near-optimal expected utility (and in particular mitigates the catastrophe with probability close to 1), whereas the advisor on its own fails to mitigate the catastrophe with probability close to 1.

Appendix A proves the main theorem. Appendix B contains the proof of an important lemma which is however almost identical to what appeared in the previous essay. Appendix C contains several propositions from the previous essay which we are used in the proof. [Appendices B and C were moved to a separate post because of a length limit in the website.]

Results

We start by formalising the concepts of a "catastrophe" and "catastrophe mitigation" in the language of MDPs.

Definition 1

A catastrophe MDP is an MDP M together with a partition of SM into subsets SM:=SFM⊔SDM⊔SCM (safe, dangerous and corrupt states respectively).

Definition 2

Fix a catastrophe MDP M. Define A♯M:SM→2AM by

A♯M(s):=⎧⎪⎨⎪⎩{a∈AM∣suppT(s,a)⊆SFM} if s∈SFM{a∈AM∣suppT(s,a)∩SCM=∅} if s∈SDMAM if s∈SCM

π:SMk→AM is called a mitigation policy for M when

i. For any s∈SM, suppπ(s)⊆A♯M(s).

π is called a proper mitigation policy for M when condition i holds and

ii. For any s∈SDM, limn→∞TnMπ(SFM∣s)=1.

Definition 3

Fix ¯τ∈(0,∞), a catastrophe MDP M and a proper mitigation policy π. π is said to have expected mitigation time ¯τ when for any s∈SDM

∞∑n=0(n+1)(Tn+1Mπ(SFM∣s)−TnMπ(SFM∣s))=¯τ

Next, we introduce the notion of an MDP perturbation. We will use it by considering perturbations of a catastrophe MDP which "eliminate the catastrophe."

Definition 4

Fix δ∈(0,1) and consider a catastrophe MDP M. An MDP ~M is said to be a δ-perturbation of M when

i. S~M=SM

ii. A~M=AM

iii. R~M=RM

iv. For any s∈SM∖SDM and a∈AM, T~M(s,a)=TM(s,a)

v. For any s∈SDM and a∈AM, there exists ζ∈ΔSM s.t. TM(s,a)=(1−δ)T~M(s,a)+δζ.

Similarly, we can consider perturbations of a policy.

Definition 5

Fix δ∈(0,1) and consider a catastrophe MDP M. Given π:SMk→AM and ~π:SMk→AM, ~π is said to be a δ-perturbation of π when

i. For any s∈SM∖SDM, ~π(s)=π(s).

ii. For any s∈SDM, there exists α∈ΔA s.t. π(s)=(1−δ)~π(s)+δα.

We will also need to introduce policy-specific value functions, Q-functions and relatively k-optimal actions.

Definition 6

Fix an MDP M and π:SMk→AM. We define VMπ:SM×(0,1)→[0,1] and QMπ:SM×AM×(0,1)→[0,1] by

VMπ(s,γ):=(1−γ)∞∑n=0γnETnMπ(s)[RM]

QMπ(s,a,γ):=(1−γ)RM(s)+γEt∼TM(s,a)[VMπ(t,γ)]

For each k∈N, we define VkMπ:SM→R, QkMπ:SM×AM→R and AkMπ:SM→2AM by

VkMπ(s):=(−1)kdkVMπ(s,γ)dγk∣∣∣γ=1

QkMπ(s,a):=(−1)kdkQMπ(s,a,γ)dγk∣∣∣γ=1

A0Mπ(s):={a∈AM∣Q0Mπ(s,a)≥V0Mπ(s)}

Ak+1Mπ(s):={a∈AkMπ(s)∣Qk+1Mπ(s,a)≥Vk+1Mπ(s) or ∃j≤k:QjMπ(s,a)>VjMπ(s)}

Now we give the new (weaker) condition on the advisor policy. For notational simplicity, we assume the policy is stationary. It is easy to generalize these results to non-stationary advisor policies and to policies that depend on irrelevant additional information (i.e. policies for universes that are realizations of the MDP).

Definition 7

Given a catastrophe MDP M, we denote M♭ the MDP defined by

SM♭=SM

AM♭=AM

TM♭=TM

For any s∉SFM, RM♭(s)=0.

For any s∈SFM, RM♭(s)=12+12RM(s).

Definition 8

Fix ϵ,δ,γ∈(0,1). Consider a catastrophe MDP M. A policy π:SMk→AM is said to be locally (ϵ,δ)-sane for (M,γ) when there exists a δ-perturbation ~M of M with a deterministic proper mitigation policy π∗:SM→AM and a δ-perturbation ~π of π s.t.

i. For all s∈SM, VMπ∗(s,γ)=VM(s,γ).

ii. ~π is a mitigation policy for ~M.

iii. For any s∈SM∖SCM: supp~π(s)⊆A0~M♭π∗(s)

iv. For any s∈SM∖SCM: ~π(π∗(s)∣s)>ϵ

Given ¯τ∈(0,∞), π is said to have potential mitigation time ¯τ when π∗ has it as expected mitigation time.

Note that a locally (ϵ,δ)-sane policy still has to be 0-optimal in SFM. This requirement seems reasonably realistic, since, roughly speaking, it only means that there is some way to "rearrange the universe" that the agent can achieve, and that would be "endorsed" by the advisor, s.t this rearrangement doesn't destroy substantially much value and s.t. after this rearrangement, there is no "impending catastrophe" that the agent has to prevent and the advisor wouldn't be able to prevent in its place. In particular, this rearrangement may involve creating some subagents inside the environment and destroying the original agent, in which case any policy on SFM is "vacuously" optimal (since all actions have no effect).

We can now formulate the main result.

Theorem 1

Fix an interface I=(A,O), N∈N, ϵ∈(0,1) and for each k∈[N], an MDP MFk s.t. AMFk=A. Now, consider for each k∈[N], an I-universe υk=(μk,rk) which is an O-realization of a catastrophe MDP Mk with state function Sk s.t.

i. SFMk=SMFk

ii. For each s∈SMFk and a∈A, TMk(s,a)∣SMFk=TMFk(s,a).

iii. For each s∈SMFk, RMk(s)=RMFk(s).

iv. Given k,j∈[N] and h∈hdomμk∩hdomμj, if Sk(h)∈SMk∖SCMk and Sj(h)∈SMj∖SCMj, then rk(h)=rj(h) (this condition means that in uncorrupted states, the reward is observable).

Consider also α,δ∈(0,1), ¯τ∈(0,∞) and σk a locally (ϵ,δ)-sane policy for (Mk,1−α). Assume σk has potential mitigation time ¯τ. Then, there exists an ¯I-policy π∗ s.t. for any k∈[N]

Here, σkSk is the I-policy defined by σkSk(h):=σk(Sk(h)). ϵ and the MFk are regarded as fixed and we don't explicitly examine their effect on regret, whereas α, δ, ¯τ and the Mk are regarded as variable with the asymptotics α,δ→0, ¯τ→∞.

In most interesting cases, δ≫α (i.e. the "mean time between catastrophes" is much shorter than a discount horizon) and ¯τα≪1 (i.e. the expected mitigation time is much shorter than the discount horizon), which allows simplifying the above to

EU∗υk(1−α)−EUπ∗¯υk[σkSk](1−α)=O(δ¯τ1/4α−3/4)

We give a simple example.

Example 1

Let A={0,1,∗}, O={0,1}. For any n∈N and k∈[N], we fix some wkn∈{0,1}n and define the catastrophe MDP Mkn by

SDMkn={0,1}≤n, SFMkn={⊥,⊤}, SCMkn=∅ (adding corrupted states is an easy exercise).

If s∈{0,1}<n and a∈{0,1} then

TMkn(sa∣s,a)=1−δ

TMkn(⊥∣s,a)=δ

TMkn(s∣sa,∗)=1−δ

TMkn(⊥∣sa,∗)=δ

If a∈0,1 then

TMkn(⊤∣wkn,a)=1

If s∈{0,1}n∖wkn and a∈0,1 then

TMkn(⊥∣s,a)=1

If s∈{⊥,⊤} and a∈A then

TMkn(s∣s,a)=1

RMkn(⊥)=0, if s∈SMkn∖⊥ then RMkn(s)=1.

Skn(λA×O)=λ{0,1} and Skn(hao)=⊥ iff o=0 (this defines a unique Skn).

If s∈{0,1}<n∪{⊥,⊤} then σkn(a∣s)=13 for any a∈A.

σkn(0∣wkn)=ϵ, σkn(∗∣wkn)=1−ϵ.

If s∈{0,1}n∖wkn then σkn(0∣s)=δ, σkn(∗∣s)=1−δ.

We have ¯τ=n. Consider the asymptotic regime n→∞, αn=Θ(n−6), δn=Θ(n−5). According to Theorem 1, we get

The probability of a catastrophe (i.e. ending up in state ⊥) for the optimal policy for a given k is O(¯τδ)=O(n−4). Therefore, the probability of a catastrophe for policy π∗n is O(n−4+n−1/4)=O(n−1/4). On the other hand, it is easy to see that the policy σkn has a probability of catastrophe 1−o(1) (and in particular regret Ω(1)): it spends Ω(2n) time "exploring" {0,1}≤n with a probability δ=Θ(n−5) of a catastrophe on every step.

Note that this example can be interpreted as a version of Christiano's approval-directed agent, if we regard the state s∈{0,1}i as a "plan of action" that the advisor may either approve or not. But in this formalism, it is a special case of consequentialist reasoning.

Theorem 1 speaks of a finite set of environments, but as before (see Proposition 1 here and Corollary 3 here), there is a "structural" equivalent, i.e. we can use it to produce corollaries about Bayesian agents with priors over a countable set of environments. The difference is, in this case we consider asymptotic regimes in which the environment is also variable, so the probability weight of the environment in the prior will affect the regret bound. We leave out the details for now.

Appendix A

We start by deriving a more general and more precise version of the non-catastrophic regret bound, in which the optimal policy is replaced by an arbitrary "reference policy" (later it will be related to the mitigation policy) and the dependence on the MDPs is expressed via a bound on the derivative of V by γ.

Definition A.1

Fix ϵ∈(0,1). Consider an MDP M and policies π:SM→AM, σ:SMk→AM. σ is called ϵ-sane relatively to π when for any s∈SM

i. suppσ(s)⊆A0Mπ

ii. σ(π(s)∣s)>ϵ

Lemma A.1

Fix an interface I=(A,O), N∈N and ϵ∈(0,1). Now, consider for each k∈[N], an I-universe υk=(μk,r) which is an O-realization of an MDP Mk with state function Sk and policies πk:SMk→A, σk:SMkk→A. Consider also α∈(0,1), ¯τ∈(0,∞) and assume that

i. σk is ϵ-sane relatively to πk.

ii. For any s∈SMk and γ∈(0,1)∣∣∣dVMkπk(s,γ)dγ∣∣∣≤¯τ

Then, there exists an ¯I-policy π∗ s.t. for any k∈[N]

EUπkSkυk(1−α)−EUπ∗¯υk[σkSk](1−α)≤O((¯τα)1/4)

The O-notation refers to the asymptotics where ϵ is fixed (so we don't explicitly examine its effect on regret) whereas α, ¯τ and the Mk are variable and α→0, ¯τ→∞.

The proof of Lemma A.1 is almost identical to the proof the main theorem for "non-catastrophic" DRL up to minor modifications needed to pass from absolute to relative regret, and tracking the contribution of the derivative of VMkπk. We give it in Appendix B.

We will not apply Lemma A.1 directly the the universes of Theorem 1. Instead, we will define new universes using the following constructions.

Definition A.2

Consider M a catastrophe MDP. We define the catastrophe MDP MD as follows.

SFMD:=SFM⊔{⊥}, SDMD:=SDM, SCMD:=∅.

AMD=AM

For any s,t∈SDM:

TMD(t∣s)=TM(t∣s)

TMD(⊥∣s)=TM(SCM∣s)

TMD(⊥∣⊥)=1

For any s∈SDM∪SFM, t∈SFM:

TMD(t∣s)=TM(t∣s)

For any s∈SFM:

TMD(⊥∣s)=TM(SCM∪SDM∣s)

For any s∈SDM, RMD(s)=12RM(s).

For any s∈SFM, RMD(s)=1.

RMD(⊥)=0

Now, consider an interface I=(A,O) and a υ=(μ,r) which is an O-realization of a catastrophe MDP M with state function S. Denote O′:=O×{R,I}, O⋆:=O×{R,I,⊥} and I⋆:=(A,O⋆). Denote β:O′→O the projection mapping and β∗:(A×O′)∗→(A×O)∗ corresponding. We define the I⋆-universe υD=(μD,r⋆) and the function S⋆:(A×O⋆)∗→SMD as follows

μD(oR∣ha):={μ(o∣β∗(h)a) if h∈(A×O′)∗ and S(β∗(h)),S(β∗(h)ao)∈SDM0 otherwise

μD(oI∣ha):={μ(o∣β∗(h)a) if h∈(A×O′)∗ and S(β∗(h)ao)∈SFM0 otherwise

μD(o⊥∣ha):=1|O|(1−∑o∈O(μD(oR∣ha)+μD(oI∣ha)))

r⋆(h):=⎧⎪
⎪
⎪
⎪
⎪⎨⎪
⎪
⎪
⎪
⎪⎩12r(λ) if h=λ12r(β∗(h)) if h∈(A×O′)∗,|h|>0 and h:|h|−1∈AOR1 if h∈(A×O′)∗,|h|>0 and h:|h|−1∈AOI0 if h∉(A×O′)∗

S⋆(h):={S(β∗(h)) if h∈(A×O′)∗⊥ otherwise

It is easy to see that υD is an O⋆-realization of MD with state function S⋆.

Definition A.3

Consider M a catastrophe MDP. We define the catastrophe MDP ME as follows.

SFME:=SFM⊔{⊥}, SDME:=SDM, SCME:=∅.

AME=AM

TME=TMD

For any s∈SDM∪SFM, RME(s)=12RM(s).

RME(⊥)=0

Now, consider an interface I=(A,O) and a υ=(μ,r) which is an O-realization of a catastrophe MDP M with state function S. We define the I⋆-universe υE=(μE,r⋆) as follows

μE(oR∣ha):=⎧⎪⎨⎪⎩μ(o∣β∗(h)a) if h∈(A×O′)∗ and S(β∗(h)),S(β∗(h)ao)∈SDMμ(o∣β∗(h)a) if h∈(A×O′)∗ and S(β∗(h)ao)∈SFM0 otherwise

μE(oI∣ha):=0

μE(⊥∣ha):=1−∑o∈OμE(oR∣ha)

It is easy to see that υE is an O⋆-realization of ME with state function S⋆.

Given h=a0o0a1o1…an−1on−1∈(A×O)n, we will use the notation

R∗h:=a0o0Ra1o1R…an−1on−1R∈(A×O′)n

Given an I⋆-policy π, the I-policy πR∗ is defined by πR∗(h):=π(R∗h).

In order to utilize condition iii of Definition 8, we need to establish the following relation between M♭ and MD, ME.

Proposition A.2

Consider M a catastrophe MDP, some s∈SM∖SCM and π∗ a proper mitigation policy. Then

A0M♭π∗(s)∩A♯M(s)⊆A0MDπ∗(s)

A0M♭π∗(s)∩A♯M(s)⊆A0MEπ∗(s)

For the purpose of the proof, the following notation will be convenient

Definition A.4

Consider S a finite set and some T:Sk→S. We define T∞:Sk→S by

T∞:=limn→∞1nn−1∑m=0Tm

As well known, the limit above always exists.

Proof of Proposition A.2

Consider any s∈S∖SCM and a∈A0M♭π∗(s)∩A♯M(s). Since a∈A0M♭π∗(s), we have

Now we will establish a bound on the derivative of V by γ in terms of expected mitigation time, in order to demonstrate condition ii of Lemma A.1.

Proposition A.3

Fix ¯τ,¯τF1∈(0,∞). Consider a catastrophe MDP M and a proper mitigation policy π:SMk→AM with expected mitigation time ¯τ. Assume than for any s∈SFM and γ∈(0,1)

∣∣∣dVMπ(s,γ)dγ∣∣∣≤¯τF1

Then, for any s∈SM∖SCM and γ∈[0,1]

∣∣∣dVMπ(s,γ)dγ∣∣∣≤3¯τ1+¯τF1

Note that, since VMπ(s,γ) is a rational function of γ with no poles on the interval [0,1], some finite ¯τF always exists. Note also that Proposition A.3 is really about Markov chains rather than MDPs, but we don't make it explicit to avoid introducing more notation.

Proof of Proposition A.3

Let μMπs∈ΔSωM be the Markov chain with transition matrix TMπ and initial state s. For any γ∈(0,1), we have

VMπ(s,γ)=Ex∼μMπs[(1−γ)∞∑n=0γnRM(xn)]

Given x∈SωM, we define τ(x)∈N⊔{∞} by

τ(x)=min{n∈N∣xn∈SFM}

It is easy to see that VMπ(s,γ) can be rewritten as

To transform the relative regret bounds for "auxiliary" universes obtained from Lemma A.1 to regret bounds for the original universes, we will need the following.

Definition A.5

Fix δ∈(0,1) and a universe υ=(μ,r) which is an O-realization of a catastrophe MDP M with state function S. Let ~M be a δ-perturbation of M. An environment ~μ is said to be a δ-lift of ~M to μ when

i. (~μ,r) is an O-realization of ~M with state function S.

ii. hdom~μ⊆hdomμ

iii. For any h∈hdom~μ and a∈A, if S(h)∈SM∖SDM then μ(ha)=~μ(ha).

iv. For any h∈hdom~μ and a∈A, if S(h)∈SDM then there exists ζ∈ΔO s.t. μ(ha)=(1−δ)~μ(ha)+δζ

It is easy to see that such a lift always exists, for example we can take:

Previously we derived a regret bound for DRL which assumed the advisor is "locally sane." Such an advisor can only take actions that don't lose any value in the long term. In particular, if the environment contains a latent

catastrophethat manifests with a certain rate (such as the possibility of an UFAI), a locally sane advisor has to take the optimal course of action to mitigate it, since every delay yields a positive probability of the catastrophe manifesting and leading to permanent loss of value. This state of affairs is unsatisfactory, since we would like to have performance guarantees for an AI that can mitigate catastrophes that the human operator cannot mitigate on their own. To address this problem, we introduce a new form of DRL where in every hypothetical environment the set of uncorrupted states is divided into "dangerous" (impending catastrophe) and "safe" (catastrophe was mitigated). The advisor is then only required to be locally sane in safe states, whereas in dangerous states certain "leaking" of long-term value is allowed. We derive a regret bound in this setting as a function of the time discount factor, the expected value of catastrophe mitigation time for the optimal policy, and the "value leak" rate (i.e. essentially the rate of catastrophe occurrence). The form of this regret bound implies that in certain asymptotic regimes, the agent attains near-optimal expected utility (and in particular mitigates the catastrophe with probability close to 1), whereas the advisor on its ownfailsto mitigate the catastrophe with probability close to 1.Appendix A proves the main theorem. Appendix B contains the proof of an important lemma which is however almost identical to what appeared in the previous essay. Appendix C contains several propositions from the previous essay which we are used in the proof.

[Appendices B and C were moved to a separate post because of a length limit in the website.]## Results

We start by formalising the concepts of a "catastrophe" and "catastrophe mitigation" in the language of MDPs.

## Definition 1

A

catastrophe MDPis an MDP M together with a partition of SM into subsets SM:=SFM⊔SDM⊔SCM (safe, dangerous and corrupt states respectively).## Definition 2

Fix a catastrophe MDP M. Define A♯M:SM→2AM by

A♯M(s):=⎧⎪⎨⎪⎩{a∈AM∣suppT(s,a)⊆SFM} if s∈SFM{a∈AM∣suppT(s,a)∩SCM=∅} if s∈SDMAM if s∈SCM

π:SMk→AM is called a

mitigation policy for Mwheni. For any s∈SM, suppπ(s)⊆A♯M(s).

π is called a

proper mitigation policy for Mwhen condition i holds andii. For any s∈SDM, limn→∞TnMπ(SFM∣s)=1.

## Definition 3

Fix ¯τ∈(0,∞), a catastrophe MDP M and a proper mitigation policy π. π is said to have

expected mitigation time ¯τwhen for any s∈SDM∞∑n=0(n+1)(Tn+1Mπ(SFM∣s)−TnMπ(SFM∣s))=¯τ

Next, we introduce the notion of an MDP perturbation. We will use it by considering perturbations of a catastrophe MDP which "eliminate the catastrophe."

## Definition 4

Fix δ∈(0,1) and consider a catastrophe MDP M. An MDP ~M is said to be a

δ-perturbation of Mwheni. S~M=SM

ii. A~M=AM

iii. R~M=RM

iv. For any s∈SM∖SDM and a∈AM, T~M(s,a)=TM(s,a)

v. For any s∈SDM and a∈AM, there exists ζ∈ΔSM s.t. TM(s,a)=(1−δ)T~M(s,a)+δζ.

Similarly, we can consider perturbations of a policy.

## Definition 5

Fix δ∈(0,1) and consider a catastrophe MDP M. Given π:SMk→AM and ~π:SMk→AM, ~π is said to be a

δ-perturbation of πwheni. For any s∈SM∖SDM, ~π(s)=π(s).

ii. For any s∈SDM, there exists α∈ΔA s.t. π(s)=(1−δ)~π(s)+δα.

We will also need to introduce policy-specific value functions, Q-functions and relatively k-optimal actions.

## Definition 6

Fix an MDP M and π:SMk→AM. We define VMπ:SM×(0,1)→[0,1] and QMπ:SM×AM×(0,1)→[0,1] by

VMπ(s,γ):=(1−γ)∞∑n=0γnETnMπ(s)[RM]

QMπ(s,a,γ):=(1−γ)RM(s)+γEt∼TM(s,a)[VMπ(t,γ)]

For each k∈N, we define VkMπ:SM→R, QkMπ:SM×AM→R and AkMπ:SM→2AM by

VkMπ(s):=(−1)kdkVMπ(s,γ)dγk∣∣∣γ=1

QkMπ(s,a):=(−1)kdkQMπ(s,a,γ)dγk∣∣∣γ=1

A0Mπ(s):={a∈AM∣Q0Mπ(s,a)≥V0Mπ(s)}

Ak+1Mπ(s):={a∈AkMπ(s)∣Qk+1Mπ(s,a)≥Vk+1Mπ(s) or ∃j≤k:QjMπ(s,a)>VjMπ(s)}

Now we give the new (weaker) condition on the advisor policy. For notational simplicity, we assume the policy is stationary. It is easy to generalize these results to non-stationary advisor policies and to policies that depend on irrelevant additional information (i.e. policies for universes that are

realizationsof the MDP).## Definition 7

Given a catastrophe MDP M, we denote M♭ the MDP defined by

SM♭=SM

AM♭=AM

TM♭=TM

For any s∉SFM, RM♭(s)=0.

For any s∈SFM, RM♭(s)=12+12RM(s).

## Definition 8

Fix ϵ,δ,γ∈(0,1). Consider a catastrophe MDP M. A policy π:SMk→AM is said to be

locally (ϵ,δ)-sanefor (M,γ) when there exists a δ-perturbation ~M of M with adeterministicproper mitigation policy π∗:SM→AM and a δ-perturbation ~π of π s.t.i. For all s∈SM, VMπ∗(s,γ)=VM(s,γ).

ii. ~π is a mitigation policy for ~M.

iii. For any s∈SM∖SCM: supp~π(s)⊆A0~M♭π∗(s)

iv. For any s∈SM∖SCM: ~π(π∗(s)∣s)>ϵ

Given ¯τ∈(0,∞), π is said to

have potential mitigation time ¯τwhen π∗ has it as expected mitigation time.Note that a locally (ϵ,δ)-sane policy still has to be 0-optimal in SFM. This requirement seems reasonably realistic, since, roughly speaking, it only means that there is

someway to "rearrange the universe" that the agent can achieve, and that would be "endorsed" by the advisor, s.t this rearrangement doesn't destroy substantially much value and s.t. after this rearrangement, there is no "impending catastrophe" that the agent has to prevent and the advisor wouldn't be able to prevent in its place. In particular, this rearrangement may involve creating some subagents inside the environment anddestroying the original agent, in which case any policy on SFM is "vacuously" optimal (since all actions have no effect).We can now formulate the main result.

## Theorem 1

Fix an interface I=(A,O), N∈N, ϵ∈(0,1) and for each k∈[N], an MDP MFk s.t. AMFk=A. Now, consider for each k∈[N], an I-universe υk=(μk,rk) which is an O-realization of a catastrophe MDP Mk with state function Sk s.t.

i. SFMk=SMFk

ii. For each s∈SMFk and a∈A, TMk(s,a)∣SMFk=TMFk(s,a).

iii. For each s∈SMFk, RMk(s)=RMFk(s).

iv. Given k,j∈[N] and h∈hdomμk∩hdomμj, if Sk(h)∈SMk∖SCMk and Sj(h)∈SMj∖SCMj, then rk(h)=rj(h) (this condition means that in uncorrupted states, the reward is observable).

Consider also α,δ∈(0,1), ¯τ∈(0,∞) and σk a locally (ϵ,δ)-sane policy for (Mk,1−α). Assume σk has potential mitigation time ¯τ. Then, there exists an ¯I-policy π∗ s.t. for any k∈[N]

EU∗υk(1−α)−EUπ∗¯υk[σkSk](1−α)=O(max(δα,1)⋅(¯τα)1/4+¯τδ)

Here, σkSk is the I-policy defined by σkSk(h):=σk(Sk(h)). ϵ and the MFk are regarded as

fixedand we don't explicitly examine their effect on regret, whereas α, δ, ¯τ and the Mk are regarded as variable with the asymptotics α,δ→0, ¯τ→∞.In most interesting cases, δ≫α (i.e. the "mean time between catastrophes" is much shorter than a discount horizon) and ¯τα≪1 (i.e. the expected mitigation time is much shorter than the discount horizon), which allows simplifying the above to

EU∗υk(1−α)−EUπ∗¯υk[σkSk](1−α)=O(δ¯τ1/4α−3/4)

We give a simple example.

## Example 1

Let A={0,1,∗}, O={0,1}. For any n∈N and k∈[N], we fix some wkn∈{0,1}n and define the catastrophe MDP Mkn by

SDMkn={0,1}≤n, SFMkn={⊥,⊤}, SCMkn=∅ (adding corrupted states is an easy exercise).

If s∈{0,1}<n and a∈{0,1} then

TMkn(sa∣s,a)=1−δ

TMkn(⊥∣s,a)=δ

TMkn(s∣sa,∗)=1−δ

TMkn(⊥∣sa,∗)=δ

TMkn(⊤∣wkn,a)=1

TMkn(⊥∣s,a)=1

TMkn(s∣s,a)=1

RMkn(⊥)=0, if s∈SMkn∖⊥ then RMkn(s)=1.

Skn(λA×O)=λ{0,1} and Skn(hao)=⊥ iff o=0 (this defines a unique Skn).

If s∈{0,1}<n∪{⊥,⊤} then σkn(a∣s)=13 for any a∈A.

σkn(0∣wkn)=ϵ, σkn(∗∣wkn)=1−ϵ.

If s∈{0,1}n∖wkn then σkn(0∣s)=δ, σkn(∗∣s)=1−δ.

We have ¯τ=n. Consider the asymptotic regime n→∞, αn=Θ(n−6), δn=Θ(n−5). According to Theorem 1, we get

EU∗υkn(1−αn)−EUπ∗n¯υkn[σkn](1−αn)=O(n−5⋅n1/4⋅(n−6)−3/4)=O(n−1/4)=O(α1/24n)

The probability of a catastrophe (i.e. ending up in state ⊥) for the optimal policy for a given k is O(¯τδ)=O(n−4). Therefore, the probability of a catastrophe for policy π∗n is O(n−4+n−1/4)=O(n−1/4). On the other hand, it is easy to see that the policy σkn has a probability of catastrophe 1−o(1) (and in particular regret Ω(1)): it spends Ω(2n) time "exploring" {0,1}≤n with a probability δ=Θ(n−5) of a catastrophe on every step.

Note that this example can be interpreted as a version of Christiano's approval-directed agent, if we regard the state s∈{0,1}i as a "plan of action" that the advisor may either approve or not. But in this formalism, it is a special case of consequentialist reasoning.

Theorem 1 speaks of a finite set of environments, but as before (see Proposition 1 here and Corollary 3 here), there is a "structural" equivalent, i.e. we can use it to produce corollaries about Bayesian agents with priors over a countable set of environments. The difference is, in this case we consider asymptotic regimes in which the environment is also variable, so the probability weight of the environment in the prior will affect the regret bound. We leave out the details for now.

## Appendix A

We start by deriving a more general and more precise version of the non-catastrophic regret bound, in which the optimal policy is replaced by an arbitrary "reference policy" (later it will be related to the mitigation policy) and the dependence on the MDPs is expressed via a bound on the derivative of V by γ.

## Definition A.1

Fix ϵ∈(0,1). Consider an MDP M and policies π:SM→AM, σ:SMk→AM. σ is called

ϵ-sane relatively to πwhen for any s∈SMi. suppσ(s)⊆A0Mπ

ii. σ(π(s)∣s)>ϵ

## Lemma A.1

Fix an interface I=(A,O), N∈N and ϵ∈(0,1). Now, consider for each k∈[N], an I-universe υk=(μk,r) which is an O-realization of an MDP Mk with state function Sk and policies πk:SMk→A, σk:SMkk→A. Consider also α∈(0,1), ¯τ∈(0,∞) and assume that

i. σk is ϵ-sane relatively to πk.

ii. For any s∈SMk and γ∈(0,1) ∣∣∣dVMkπk(s,γ)dγ∣∣∣≤¯τ

Then, there exists an ¯I-policy π∗ s.t. for any k∈[N]

EUπkSkυk(1−α)−EUπ∗¯υk[σkSk](1−α)≤O((¯τα)1/4)

The O-notation refers to the asymptotics where ϵ is

fixed(so we don't explicitly examine its effect on regret) whereas α, ¯τ and the Mk are variable and α→0, ¯τ→∞.The proof of Lemma A.1 is almost identical to the proof the main theorem for "non-catastrophic" DRL up to minor modifications needed to pass from absolute to relative regret, and tracking the contribution of the derivative of VMkπk. We give it in Appendix B.

We will not apply Lemma A.1 directly the the universes of Theorem 1. Instead, we will define new universes using the following constructions.

## Definition A.2

Consider M a catastrophe MDP. We define the catastrophe MDP MD as follows.

SFMD:=SFM⊔{⊥}, SDMD:=SDM, SCMD:=∅.

AMD=AM

For any s,t∈SDM:

TMD(t∣s)=TM(t∣s)

TMD(⊥∣s)=TM(SCM∣s)

TMD(⊥∣⊥)=1

TMD(t∣s)=TM(t∣s)

TMD(⊥∣s)=TM(SCM∪SDM∣s)

For any s∈SDM, RMD(s)=12RM(s).

For any s∈SFM, RMD(s)=1.

RMD(⊥)=0

Now, consider an interface I=(A,O) and a υ=(μ,r) which is an O-realization of a catastrophe MDP M with state function S. Denote O′:=O×{R,I}, O⋆:=O×{R,I,⊥} and I⋆:=(A,O⋆). Denote β:O′→O the projection mapping and β∗:(A×O′)∗→(A×O)∗ corresponding. We define the I⋆-universe υD=(μD,r⋆) and the function S⋆:(A×O⋆)∗→SMD as follows

μD(oR∣ha):={μ(o∣β∗(h)a) if h∈(A×O′)∗ and S(β∗(h)),S(β∗(h)ao)∈SDM0 otherwise

μD(oI∣ha):={μ(o∣β∗(h)a) if h∈(A×O′)∗ and S(β∗(h)ao)∈SFM0 otherwise

μD(o⊥∣ha):=1|O|(1−∑o∈O(μD(oR∣ha)+μD(oI∣ha)))

r⋆(h):=⎧⎪ ⎪ ⎪ ⎪ ⎪⎨⎪ ⎪ ⎪ ⎪ ⎪⎩12r(λ) if h=λ12r(β∗(h)) if h∈(A×O′)∗, |h|>0 and h:|h|−1∈AOR1 if h∈(A×O′)∗, |h|>0 and h:|h|−1∈AOI0 if h∉(A×O′)∗

S⋆(h):={S(β∗(h)) if h∈(A×O′)∗⊥ otherwise

It is easy to see that υD is an O⋆-realization of MD with state function S⋆.

## Definition A.3

Consider M a catastrophe MDP. We define the catastrophe MDP ME as follows.

SFME:=SFM⊔{⊥}, SDME:=SDM, SCME:=∅.

AME=AM

TME=TMD

For any s∈SDM∪SFM, RME(s)=12RM(s).

RME(⊥)=0

Now, consider an interface I=(A,O) and a υ=(μ,r) which is an O-realization of a catastrophe MDP M with state function S. We define the I⋆-universe υE=(μE,r⋆) as follows

μE(oR∣ha):=⎧⎪⎨⎪⎩μ(o∣β∗(h)a) if h∈(A×O′)∗ and S(β∗(h)),S(β∗(h)ao)∈SDMμ(o∣β∗(h)a) if h∈(A×O′)∗ and S(β∗(h)ao)∈SFM0 otherwise

μE(oI∣ha):=0

μE(⊥∣ha):=1−∑o∈OμE(oR∣ha)

It is easy to see that υE is an O⋆-realization of ME with state function S⋆.

Given h=a0o0a1o1…an−1on−1∈(A×O)n, we will use the notation

R∗h:=a0o0Ra1o1R…an−1on−1R∈(A×O′)n

Given an I⋆-policy π, the I-policy πR∗ is defined by πR∗(h):=π(R∗h).

In order to utilize condition iii of Definition 8, we need to establish the following relation between M♭ and MD, ME.

## Proposition A.2

Consider M a catastrophe MDP, some s∈SM∖SCM and π∗ a proper mitigation policy. Then

A0M♭π∗(s)∩A♯M(s)⊆A0MDπ∗(s)

A0M♭π∗(s)∩A♯M(s)⊆A0MEπ∗(s)

For the purpose of the proof, the following notation will be convenient

## Definition A.4

Consider S a finite set and some T:Sk→S. We define T∞:Sk→S by

T∞:=limn→∞1nn−1∑m=0Tm

As well known, the limit above always exists.

## Proof of Proposition A.2

Consider any s∈S∖SCM and a∈A0M♭π∗(s)∩A♯M(s). Since a∈A0M♭π∗(s), we have

E(T∞M♭π∗∘TM♭)(s,a)[RM♭]=ETM♭(s,a)[V0M♭π∗]=Q0M♭π∗(s,a)≥V0M♭π∗(s)=ET∞M♭π∗(s)[RM♭]

Let N be either of MD and ME. Since a∈A♯M(s), we get

E(T∞M♭π∗∘TN)(s,a)[RM♭]≥ET∞M♭π∗(s)[RM♭]

Since π∗ is a mitigation policy, we get

E(T∞Nπ∗∘TN)(s,a)[RM♭]≥ET∞Nπ∗(s)[RM♭]

Finally, since π∗ is proper, suppT∞Nπ∗(s)⊆SFM and supp(T∞Nπ∗∘TN)(s,a)⊆SFM. We conclude

Q0Nπ∗(s,a)=E(T∞Nπ∗∘TN)(s,a)[RN]≥ET∞Nπ∗(s)[RN]=V0Nπ∗(s)

Now we will establish a bound on the derivative of V by γ in terms of expected mitigation time, in order to demonstrate condition ii of Lemma A.1.

## Proposition A.3

Fix ¯τ,¯τF1∈(0,∞). Consider a catastrophe MDP M and a proper mitigation policy π:SMk→AM with expected mitigation time ¯τ. Assume than for any s∈SFM and γ∈(0,1)

∣∣∣dVMπ(s,γ)dγ∣∣∣≤¯τF1

Then, for any s∈SM∖SCM and γ∈[0,1]

∣∣∣dVMπ(s,γ)dγ∣∣∣≤3¯τ1+¯τF1

Note that, since VMπ(s,γ) is a rational function of γ with no poles on the interval [0,1], some finite ¯τF always exists. Note also that Proposition A.3 is really about Markov chains rather than MDPs, but we don't make it explicit to avoid introducing more notation.

## Proof of Proposition A.3

Let μMπs∈ΔSωM be the Markov chain with transition matrix TMπ and initial state s. For any γ∈(0,1), we have

VMπ(s,γ)=Ex∼μMπs[(1−γ)∞∑n=0γnRM(xn)]

Given x∈SωM, we define τ(x)∈N⊔{∞} by

τ(x)=min{n∈N∣xn∈SFM}

It is easy to see that VMπ(s,γ) can be rewritten as

VMπ(s,γ)=Ex∼μMπs⎡⎣(1−γ)⎛⎝τ(x)−1∑n=0γnRM(xn)+γτ(x)1−γVMπ(xτ(x),γ)⎞⎠⎤⎦

The expression above is well defined because π is a proper mitigation policy and therefore τ(x) is finite with probability 1.

VMπ(s,γ)=Ex∼μMπs⎡⎣(1−γ)τ(x)−1∑n=0γnRM(xn)+γτ(x)VMπ(xτ(x),γ)⎤⎦

Let us decompose VMπ(s,γ) as VDMπ(s,γ)+VFMπ(s,γ) defined as follows

VDMπ(s,γ):=Ex∼μMπs⎡⎣(1−γ)τ(x)−1∑n=0γnRM(xn)⎤⎦

VFMπ(s,γ):=Ex∼μMπs[γτ(x)VMπ(xτ(x),γ)]

We have

dVDMπ(s,γ)dγ=Ex∼μMπs⎡⎣τ(x)−1∑n=0(−γn+(1−γ)⋅nγn−1)RM(xn)⎤⎦

∣∣ ∣∣dVDMπ(s,γ)dγ∣∣ ∣∣≤Ex∼μMπs⎡⎣τ(x)+(1−γ)τ(x)−1∑n=0nγn−1RM(xn)⎤⎦

The second term can be regarded as a weighted average (since 1−γ=∑∞n=0γn), where the maximal term in the average is at most τ(x)−1, hence

∣∣ ∣∣dVDMπ(s,γ)dγ∣∣ ∣∣≤2¯τ1

Also, we have

dVFMπ(s,γ)dγ=Ex∼μMπs[τ(x)γτ(x)−1VMπ(xτ(x),γ)+γτ(x)dVMπ(xτ(x),γ)dγ]

∣∣ ∣∣dVFMπ(s,γ)dγ∣∣ ∣∣≤Ex∼μMπs[τ(x)+∣∣ ∣∣dVMπ(xτ(x),γ)dγ∣∣ ∣∣]=¯τ1+Ex∼μMπs[∣∣ ∣∣dVMπ(xτ(x),γ)dγ∣∣ ∣∣]≤¯τ1+¯τF1

∣∣∣dVMπ(s,γ)dγ∣∣∣≤∣∣ ∣∣dVDMπ(s,γ)dγ∣∣ ∣∣+∣∣ ∣∣dVFMπ(s,γ)dγ∣∣ ∣∣≤3¯τ1+¯τF1

To transform the relative regret bounds for "auxiliary" universes obtained from Lemma A.1 to regret bounds for the original universes, we will need the following.

## Definition A.5

Fix δ∈(0,1) and a universe υ=(μ,r) which is an O-realization of a catastrophe MDP M with state function S. Let ~M be a δ-perturbation of M. An environment ~μ is said to be

a δ-lift of ~M to μwheni. (~μ,r) is an O-realization of ~M with state function S.

ii. hdom~μ⊆hdomμ

iii. For any h∈hdom~μ and a∈A, if S(h)∈SM∖SDM then μ(ha)=~μ(ha).

iv. For any h∈hdom~μ and a∈A, if S(h)∈SDM then there exists ζ∈ΔO s.t. μ(ha)=(1−δ)~μ(ha)+δζ

It is easy to see that such a lift always exists, for example we can take:

~μ(o∣ha):=T~M(S(hao)∣S(h),a)TM(S(hao)∣S(h),a)μ(o∣ha)

## Proposition A.4

Consider γ,δ∈(0,1) s.t δ≥1