speaker: Pavel Temirchev

 

RL as Probabilistic Inference

What we will NOT discuss today?

What we WILL discuss

How to treat RL problem as a probabilistic inference problem?

\mathbb{E}_\pi \sum_{t=0}^T r(s_t, a_t) \rightarrow \max_\pi

Standard RL: optimization

Probabilistic Inference

A
B
p(A|B) = \;?

WE

\pi(a_t| s_t, \pi \;\text{is optimal})

may be something like this will do...

Why we will discuss it?

  • Treating RL as inference can help at using effective inference tools for solving RL problems.
    We can develop new algorithms.
     

  • Bayesians are always try to generalize other's ideas.
     

  • As we will see, Inference has a close connection to
    Maximum Entropy RL - may be it will help to improve exploration!

     

Background

Probabilistic Graphical Models

p(a, b, c, d, e)

Generally, a joint probability distribution

p(a, b, c, d, e) = p(a|b, c, d, e) p(b|c, d, e) p(c|d, e) p(d|e) p(e)

can be factorized as follows:

Graphical representation of a probabilistic model can help

to embed structure into the model:

a
b
c
d
e
p(a, b, c, d, e) = p(a) p(b) p(c|a, b) p(d|c) p(e|c)

Background

Inference on PGMs

Inference:

p(z_1) = \int p(z_1|x_1) p(x_1) dx_1
p(x_2) = \int p(x_2|x_1) p(x_1) dx_1

Graphical representation can help make probabilistic inference more easily.

There are a lot of algorithms for exact and approximate inference for PMGs

We will discuss very simple example of
Message Passing Algorithm on trees.

p(z_l) = \;? \;\;\; \forall l

Question:

Model:

p(x_{0:L}, z_{0:L}) = p(x_0)p(z_0|x_0)\prod_l p(x_l|x_{l-1})p(z_l|x_l)
z_0
x_0
z_1
x_1
z_L
x_L
\dots
p(z_0) = \int p(z_0|x_0) p(x_0) dx_0
p(x_1) = \int p(x_1|x_0) p(x_0) dx_0
p(z_l) = \int p(z_l|x_l) p(x_l) dx_l
p(x_{l+1}) = \int p(x_{l+1}|x_l) p(x_l) dx_l

Background

Bayes' Rule and Kulback-Leibler divergence (KL)

Bayes' Rule: allows to calculate posterior distribution of a r.v.
given new data and a prior distribution

Kulback-Leibler divergence is a measure of how one distribution is different from another, reference, distribution (not symmetric):

p(z|x) =
\frac{p(x|z) p(z)}{p(x)}
=
\frac{p(x|z) p(z)}{\int p(x|z)p(z)dz}
\text{KL}\Big( q(x)\; \big|\big|\; p(x) \Big) = \mathbb{E}_{x \sim q} \Big[ \log \frac{q(x)}{p(x)} \Big]

Background

Approximate probabilistic inference: Variational Inference (VI)

When applying Bayes' rule, the common situation

is intractability of the evidence term

p(x) = \int p(x|z)p(z)dz

Hence, the exact posterior                is intractable!

One way to go is to use approximate inference procedure called Variational Inference

p(z|x)
\text{KL}\Big( q(z)\; \big|\big|\; p(z|x) \Big) \rightarrow \min_{q \in \mathcal{Q}}

We want to minimize the dissimilarity between the true posterior

and our approximation - variational distribution

The search is in the chosen family of variational distributions

p(z|x)
q(z)
q \in\mathcal{Q}

Background

Approximate probabilistic inference: Variational Inference (VI)

It can be shown that the defined minimization problem is closely related

to the maximization of some lower bound on the evidence

p(x) = \int p(x|z)p(z)dz

We can rewrite the logarithm of the evidence as follows:

\log p(x) = \text{KL}\Big( q(z)\; \big|\big|\; p(z|x) \Big) + \mathcal{L}(q)

Where            

              is so-called Evidence Lower Bound Objective (ELBO)

\mathcal{L}(q) = - \mathbb{E}_q \Big[\log q(z) - \log p(x, z) \Big]
(1)

The LHS of         is independent of     , whereas each term on the RHS is dependent.

(1)
q

Hence, the minimization of         is equal to the maximization of ELBO

\text{KL}
\mathcal{L}(q)
\text{KL}\big( q\; ||\; p \big) \rightarrow \min_{q \in \mathcal{Q}}

It is your choice: either you want to minimize:
 

                                     or you want to maximize:

\mathcal{L}(q) \rightarrow \max_{q \in \mathcal{Q}}

Background

RL Basics

Markov process:

p(\tau) = p(s_0) \prod_{t=0}^T p(a_t|s_t) p(s_{t+1}|s_t, a_t)

Maximization problem:

\pi^\star = \arg\max_\pi \sum_{t=0}^T \mathbb{E}_{s_t, a_t \sim \pi} [r(s_t, a_t)]
Q^\pi(s_t,a_t) := r(s_t,a_t) + \sum_{t'=t+1}^T \mathbb{E}_{s_{t'}, a_{t'} \sim \pi} [r(s_{t'}, a_{t'})]

Value functions (defined for policy):

Q^\star(s_t,a_t) = r(s_t,a_t) + \mathbb{E}_{s_{t+1}} V^\star(s_{t+1})

Bellman Optimality operator:

V^\star(s_t) = \max_a Q^\star(s_{t}, a)

Probabilistic Graphical Model

for MDP

a_0
s_0
a_1
s_1
a_2
s_2
V^\pi(s_t) = \mathbb{E}_a Q^\pi(s_{t}, a)

Generally, reward is a random variable:

r(s_t,a_t) = \mathbb{E} \big[ R(s_t, a_t) \big]

A heuristic for better exploration

Maximum entropy RL

a_t \sim \mathcal{N}(\cdot| \pi^\star, \sigma^2)

Standard Policy Gradient:

a_t \sim \exp{Q(s_t, a_t)}

Policy "proportional" to Q:

How to find such a policy?

\min_\pi\text{KL}\Big(\pi(\cdot|s_0)||\exp{Q(s_0, \cdot)}\Big) =
\max_\pi \mathbb{E}_\pi \Big[ Q(s_0, a_0) - \log \pi(a_0|s_0) \Big] =
\max_\pi \mathbb{E}_\pi \Big[ \sum_t^T r(s_t, a_t) {\color{pink}+ \mathcal{H} \big( \pi(\cdot|s_0) \big)}\Big]
Q^\star(s_0, \cdot)

go left

go right

a_0
\exp Q^\star(s_0, \cdot)
\mathcal{N}(\cdot|\arg\max Q^\star, \sigma^2)

It is very similar to the heuristic Maximum Entropy RL objective

\max_\pi \mathbb{E}_\pi \Big[ \sum_t^T r(s_t, a_t) {\color{pink}+ \mathcal{H} \big( \pi(\cdot|s_t) \big)}\Big]

During the lecture we will derive a probabilistic model inference on which results in Maximum Entropy RL objective

RL as Probabilistic Inference

Graphical Model with Optimality variables

a_0
s_0
a_1
s_1
\mathcal{O}_0
\mathcal{O}_1
p(\mathcal{O}_t =1 |s_t, a_t) := p(\mathcal{O}_t |s_t, a_t)

What if we would have binary optimality variables?

Let us look at the PGM for an MDP

\mathcal{O}_2
a_2
s_2

If                 then timestep     was optimal.

\mathcal{O}_t = 1
t

Probability that the               pair is optimal:

(s_t, a_t)
p(\mathcal{O}_t =1 |s_t, a_t) := p(\mathcal{O}_t |s_t, a_t) = \exp\big(r(s_t,a_t)\big)

But how we should define this probability?

Use exponentiation. Exponents are good.

Let us analyze the distribution of trajectories conditioned on optimality:

p(\tau|\mathcal{O}_{0:T}) \propto p(\tau,\mathcal{O}_{0:T}) = p(s_0)\prod_{t=0}^Tp(a_t|s_t)p(s_{t+1}|s_t,a_t) \exp\big(r(s_t, a_t)\big)
p({\color{#00ff00}\tau}|{\color{#ff0000}\mathcal{O}_{0:T}}) \propto p({\color{#00ff00}\tau},{\color{#ff0000}\mathcal{O}_{0:T}}) = {\color{#00ff00} p(s_0)\prod_{t=0}^Tp(a_t|s_t)p(s_{t+1}|s_t,a_t)} {\color{#ff0000}\,\exp\big(r(s_t, a_t)\big)}

RL as Probabilistic Inference

Exact inference for Optimal actions

p(a_t|s_t, \mathcal{O}_{0:T}) = p(a_t|s_t, \mathcal{O}_{t:T})
=
\frac{p(\mathcal{O}_{t:T}|s_t, a_t) p(a_t|s_t) p(s_t)}{p(\mathcal{O}_{t:T})}
\frac{p(\mathcal{O}_{t:T})}{p(\mathcal{O}_{t:T}|s_t) p(s_t)}

here                  - some prior (non-informative) policy

p(a_t|s_t)

if we set                             , then

the optimal policy is the following:

p(a_t|s_t) = \frac{1}{|\mathcal{A}|}
p(a_t|s_t, \mathcal{O}_{t:T}) \propto

We can now infer actions conditioned on optimality - optimal policy

\frac{ p(s_t, a_t|\mathcal{O}_{t:T})}{ p(s_t|\mathcal{O}_{t:T})}
(*)

      is conditionally

independent of 

given

due to the structure of PGM

(*)
a_t
\mathcal{O}_{0:t-1}
s_t
=
=
\frac{ \color{#00ff00} p(s_t, a_t|\mathcal{O}_{t:T})}{ \color{#ff0000} p(s_t|\mathcal{O}_{t:T})}

let's apply Bayes rule!

\frac{p(\mathcal{O}_{t:T}|s_t, a_t) p(a_t|s_t) p(s_t)}{p(\mathcal{O}_{t:T})}\frac{p(\mathcal{O}_{t:T})}{p(\mathcal{O}_{t:T}|s_t) p(s_t)}
p(a_t|s_t, \mathcal{O}_{0:T})
\frac{p(\mathcal{O}_{t:T}|s_t, a_t) }{p(\mathcal{O}_{t:T}|s_t)}

Exact inference for optimal actions

Message Passing Algorithm

Let's introduce
new notation:

\alpha_t(s_t, a_t) := p(\mathcal{O}_{t:T}|s_t, a_t)
\beta_t(s_t) := p(\mathcal{O}_{t:T}|s_t) = \int \alpha_t(s_t, a_t) p(a_t|s_t)da_t

We can find

all the       and       via

Message Passing algorithm:

\alpha_t
\beta_t

For the timestep      :

T
\alpha_T(s_T, a_T) = \exp(r(s_T, a_T))
\beta_T(s_T) = \int \alpha_T(s_T, a_T) p(a_T|s_T)da_T

Recursively:

\alpha_t(s_t, a_t) = \int \beta_{t+1}(s_{t+1}) \exp(r(s_t, a_t)) p(s_{t+1}|s_t, a_t)ds_{t+1}
\beta_t(s_t) = \int \alpha_t(s_t, a_t) p(a_t|s_t)da_t

We want to compute                             and 

for all

p(a_t|s_t, \mathcal{O}_{t:T}) \propto
\frac{p(\mathcal{O}_{t:T}|s_t, a_t) }{p(\mathcal{O}_{t:T}|s_t)}
p(\mathcal{O}_{t:T}|s_t, a_t)
p(\mathcal{O}_{t:T}|s_t)
0 \le t \le T

Introducing \( Q^{soft} \) and \( V^{soft} \) functions

Log-scale messages

Q^{soft}(s_t, a_t) := \log\alpha_t(s_t, a_t)
V^{soft}(s_t) := \log\beta_t(s_t)

Substituting into the recursive relation, we will obtain the following:

V^{soft}(s_t) =\log \mathbb{E}_{p(a_t|s_t)} [\exp Q^{soft}(s_t, a_t)]

soft maximum

Q^{soft}(s_t, a_t) = r(s_t, a_t) + \log \mathbb{E}_{p(s_{t+1}|s_t, a_t)} [\exp V^{soft}(s_{t+1})]

kinda Bellman equation

We can find analogues in the log-scale:

approximates hard maximum with

Q^{soft}(s_t, a_t) \rightarrow \infty

Compare \( (Q^\star,\;V^\star) \) with \( (Q^{soft},\;V^{soft}) \)

Hard approach vs. Soft approach

"Hard"        and        functions:

V^\star(s_t) =\max_{a_t} Q^\star(s_t, a_t)
Q^\star(s_t, a_t) = r(s_t, a_t) + \mathbb{E}_{p(s_{t+1}|s_t, a_t)} V^\star(s_{t+1})
V^{soft}(s_t) =\log \mathbb{E}_{p(a_t|s_t)} [\exp Q^{soft}(s_t, a_t)]
Q^{soft}(s_t, a_t) = r(s_t, a_t) + \log \mathbb{E}_{p(s_{t+1}|s_t, a_t)} [\exp V^{soft}(s_{t+1})]

"Soft" analogues:

Q^\star(s_t, a_t) = r(s_t, a_t) + \mathbb{E}_{p(s_{t+1}|s_t, a_t)} \max_{a_{t+1}} Q^\star(s_{t+1}, a_{t+1})
Q^{soft}(s_t, a_t) \approx r(s_t, a_t) + \max_{s_{t+1}} \max_{a_{t+1}} Q^{soft}(s_{t+1}, a_{t+1})
\max_{s_{t+1}}
V^\star
Q^\star

Why we are so optimistic?

p(\tau|\mathcal{O}_{0:T}) = p(s_0|\mathcal{O}_{0:T})\prod_{t=0}^T{\color{#00ff00}p(a_t|s_t,\mathcal{O}_{t:T})}p(s_{t+1}|s_t,a_t,\mathcal{O}_{t+1:T})
p(\tau|\mathcal{O}_{0:T}) = p(s_0|{\color{#ff0000}\mathcal{O}_{0:T}})\prod_{t=0}^Tp(a_t|s_t,\mathcal{O}_{t:T})p(s_{t+1}|s_t,a_t,{\color{#ff0000}\mathcal{O}_{t+1:T}})

What we have done is the inference of the policy term

which was taken from the formula for optimal trajectories distribution:

But who are the neighbors of the policy?

This policy is optimal only in the presence of optimal dynamics!

Can we fix it?

Variational Inference

Approximate inference for achievable trajectories via VI

The trajectories                                are not really achievable

since they are based on the optimistic dynamics

Our policy     , however, will be exploited with the prior dynamics:

And we want policy      to produce trajectories                     ,
which are as close as possible to optimal trajectories      

This is a Variational Inference problem:

\tau \sim p(\tau|\mathcal{O}_{0:T})
p(s_{t+1}|s_t, a_t, \mathcal{O}_{t+1:T})
\pi
q(\tau) = p(s_0)\prod_{t=0}^T \pi(a_t|s_t)p(s_{t+1}|s_t,a_t)
\pi
\tau \sim q(\tau)
\tau \sim p(\tau|\mathcal{O}_{0:T})
\text{KL}\big(q(\tau)\;||\;p(\tau|\mathcal{O}_{0:T})\big) \rightarrow \min_\pi

Variational Inference

Approximate inference for achievable trajectories via VI

Let us expand VI objective using the definition of KL-divergence:

\min_\pi \text{KL}\big(q(\tau)||p(\tau|\mathcal{O}_{0:T})\big) = - \min_\pi \mathbb{E}_q \log \frac{p(\tau,\;\mathcal{O}_{0:T})}{q(\tau)\;p(\mathcal{O}_{0:T})} = \max_\pi \mathbb{E}_q \log \frac{p(\tau,\;\mathcal{O}_{0:T})}{q(\tau)} + \text{const}

this is Maximum Entropy RL Objective

\max_\pi \mathbb{E}_q \log \frac{p(\tau,\;\mathcal{O}_{0:T})}{q(\tau)}= \max_\pi \mathbb{E}_q \Big[ \log p(s_0)+\sum_{t} \big( \log p(s_{t+1}|s_t,a_t) + r(s_t, a_t) \big) -
- \log p(s_0)-\sum_{t} \big( \log p(s_{t+1}|s_t,a_t) - \log \pi(a_t| s_t) \big) \Big]=
= \max_\pi \mathbb{E}_\pi \sum_{t}\Big[ r(s_t, a_t) + \mathcal{H}\big( \pi(\cdot| s_t)\big) \Big]

Returning to \( Q^{soft} \) and \( V^{soft} \) functions

Risk-neutral Soft approach

The objective from the previous slide can be rewritten as follows:

V^{soft}(s_t) =\log \int \exp Q^{soft}(s_t, a_t) da_t
Q^{soft}(s_t, a_t) = r(s_t, a_t) + \mathbb{E}_{p(s_{t+1}|s_t, a_t)} V^{soft}(s_{t+1})

check it yourself!

\pi(a_t|s_t) =\frac{\exp(Q^{soft}(s_t, a_t))}{\exp(V^{soft}(s_t))}
\sum_{t=0}^T\mathbb{E}_{s_t} \Big[ -\text{KL}\Big(\pi(a_t|s_t)||\frac{\exp(Q^{soft}(s_t, a_t))}{\exp(V^{soft}(s_t))}\Big) + V^{soft}(s_t) \Big] \rightarrow \max_\pi

Hence, the optimal policy is:

but with a bit changed              and              functions:

-  soft maximum

-  normal Bellman equation

Q^{soft}
V^{soft}

RL as Inference with function approximators

Maximum Entropy Policy Gradients

RL as Inference with function approximators

\mathbb{E}_{\tau \sim \pi_\theta} \sum_{t=0}^T\Big[ r(s_t, a_t) + \mathcal{H}\big(\pi_\theta(\cdot|s_t)\big) \Big] \rightarrow \max_\theta

We can directly maximize entropy-augmented objective over policy parameters     :

For gradients, use log-derivative trick:

\sum_{t=0}^T\mathbb{E}_{(s_t,a_t) \sim q_\theta} \Big[ \nabla_\theta \log\pi_\theta(a_t|s_t) \sum_{t'=t}^T\Big( r(s_{t'}, a_{t'}) -\log\pi_\theta(a_{t'}|s_{t'}) - b(s_{t'}) \Big)\Big]
\theta

  • on-policy

  • unimodal policies for continuous actions

Policy       is parametrized with a neural network with parameters

\theta
\pi
\pi_\theta(a|s) = \mathcal{N}\Big(a\;\big|\;\mu_\theta(s), \;\sigma^2 \Big)

Soft Q-learning

RL as Inference with function approximators

Train Q-network with parameters      :

\phi
\mathbb{E}_{(s_t,a_t, s_{t+1}) \sim \mathcal{D}} \Big[ Q^{soft}_\phi(s_t, a_t) - \Big( r(s_t, a_t) + V^{soft}_\phi(s_{t+1})\Big) \Big]^2\rightarrow \min_\phi

use replay buffer

where

V^{soft}_\phi(s_t) =\log \int \exp Q^{soft}_\phi(s_t, a_t) da_t

for continuous actions use

Importance Sampling

Policy is implicit

\pi(a_t|s_t) = \exp\big(Q^{soft}_\phi(s_t, a_t) - V^{soft}_\phi(s_t)\big)

for samples use

Stein Variational Gradient Descent

or MCMC :D

Soft Q-learning

Soft Actor-Critic

RL as Inference with function approximators

Train              and               networks jointly with policy

\mathbb{E}_{(s_t,a_t, s_{t+1}) \sim \mathcal{D}} \Big[ Q^{soft}_\phi(s_t, a_t) - \Big( r(s_t, a_t) + V^{soft}_\psi(s_{t+1})\Big) \Big]^2\rightarrow \min_\phi

Q-network loss:

V-network loss:

\hat{V}^{soft}(s_t) = \mathbb{E}_{a_t \sim \pi_\theta} \Big[ Q^{soft}_\phi(s_t, a_t) - \log\pi_\theta(a_t|s_t) \Big]
\mathbb{E}_{s_t \sim \mathcal{D}} \Big[ \hat{V}^{soft}(s_t) - V^{soft}_\psi(s_{t}) \Big]^2 \rightarrow \min_\psi

Objective for the policy:

\mathbb{E}_{s_t \sim \mathcal{D}, \;a_t \sim \pi_\theta} \Big[ Q^{soft}_\phi(s_t, a_t) -\log\pi_\theta(a_{t'}|s_{t})\Big] \rightarrow \max_\theta
\mathbb{E}_{(s_t,a_t, s_{t+1}) \sim \mathcal{D}} \Big[ Q^{soft}_\phi(s_t, a_t) - \Big( r(s_t, a_t) + V^{soft}_\psi(s_{t+1})\Big) \Big]^2\rightarrow \min_\phi

Q-network loss:

V-network loss:

\hat{V}^{soft}(s_t) = \mathbb{E}_{a_t \sim \pi_\theta} \Big[ Q^{soft}_\phi(s_t, a_t) - \log\pi_\theta(a_t|s_t) \Big]
\mathbb{E}_{s_t \sim \mathcal{D}} \Big[ \hat{V}^{soft}(s_t) - V^{soft}_\psi(s_{t}) \Big]^2 \rightarrow \min_\psi

Objective for the policy:

\mathbb{E}_{s_t \sim \mathcal{D}, \;a_t \sim \pi_\theta} \Big[ Q^{soft}_\phi(s_t, a_t) -\log\pi_\theta(a_{t'}|s_{t})\Big] \rightarrow \max_\theta
Q^{soft}_\phi
V^{soft}_\psi
\pi_\theta

Soft Actor-Critic

All is good. Stop?

Let us discuss simple Multi-Armed Bandit problem

Not these bandits

All is good. Stop?

Let us discuss simple Multi-Armed Bandit problem

\mathcal{S} = \empty
\mathcal{A} = \{1, \;2, \;\dots, \;N\}
​Bandit №1
​Bandit №2

​Bandit №3

. . .

​Bandit №N

COVID-19

These bandits

We assume an epistemic uncertainty associated with MDP.

We can model it via sampling MDP from some prior distribution over MDPs:

\mathcal{M} = \{ M^+, M^-\}

Sample                     and learn in      episodes
of interaction

M \sim \mathcal{M}
L

a = 1

a = 2

a = 3

...

a = N

REWARD

M^+
M^-
1
1
2
-2
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon

Multi-Armed Bandit example

How Soft Q-learning will deal with it?

\mathcal{S} = \empty
\mathcal{A} = \{1, \;2, \;\dots, \;N\}

We assume an epistemic uncertainty associated with MDP.

We can model it via sampling MDP from some prior distribution over MDPs:

\mathcal{M} = \{ M^+, M^-\}

Sample                     and learn in      episodes
of interaction

M \sim \mathcal{M}
L

a = 1

a = 2

a = 3

...

a = N

REWARD

M^+
M^-
1
1
2
-2
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon

Let us compute             and             :

Q^{soft}
V^{soft}
V^{soft}(s_t) =\log \sum_{a_t} \exp Q^{soft}(s_t, a_t)
Q^{soft}(s_t, a_t) = \mathbb{E}[R(s_t, a_t)] + \mathbb{E}_{p(s_{t+1}|s_t, a_t)} V^{soft}(s_{t+1})
Q^{soft}(a) = \mathbb{E}[R(a)]
V^{soft} =\log \sum_a \exp Q^{soft}(a)

Multi-Armed Bandit example

How Soft Q-learning will deal with it?

a = 1

a = 2

a = 3

...

a = N

REWARD

M^+
M^-
1
1
2
-2
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon
1-\epsilon

Let us compute             for all actions

and             :

Q^{soft}
V^{soft}
Q^{soft}(a) = \mathbb{E}[R(a)]
V^{soft} =\log \sum_a \exp Q^{soft}(a)
Q^{soft}
1
0
1-\epsilon
1-\epsilon
1-\epsilon
\dots

a = 1

a = 2

a = 3

a = N

For                

N = 3
V^{soft} =1.86
\pi(2) = 0.16

For                

 
N = 10
V^{soft} =3.23
\pi(2) = 0.04

For                

N = 100
V^{soft} =5.59
\pi(2) = 0.004

Reminder

Regret

\text{Regret}(M, \text{alg}, L) = \mathbb{E}_{\tau \sim M, \;\text{alg}} \Bigg[\sum_{l=0}^L \Bigg( V^\star(s_0^l) - \sum_{t=0}^T r(s_t^l, a_t^l) \Bigg) \Bigg]

In the case of epistemic uncertainty about the MDP, more general quantities are used.

The uncertainty is represented via a set of possible MDPs

(with associated probabilities of being in a concrete MDP      )

\text{BayesRegret}(\phi, \text{alg}, L) = \mathbb{E}_{M \sim \mathcal{M}} \;\text{Regret}(M, \text{alg}, L)
\text{WorstCaseRegret}(\mathcal{M}, \text{alg}, L) = \max_{M \in \mathcal{M}}\;\text{Regret}(M, \text{alg}, L)
\mathcal{M}
\phi
L
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Regret is a measure of the suboptimality of an agent.

It depends on the number of episodes      seen during learning.

Regret is defined for an algorithm         and for an MDP

Drawbacks of "RL as inference" framework

  • Soft algorithms do not consider any epistemic uncertainty about the environment.
    Nor they can resolve this uncertainty.
     

  • Soft algorithms has no guaranties on regret.
    Moreover, they show either linear or exponential regret
    on a toy problems
     

  • It is hard to tune the temperature parameter

K-learning

Variational Bayesian RL with Regret Bounds

One way to improve exploration of epistemic uncertainty is to
force agent to maximize not the expected return,
but the expected convex utility function of the return:

u(X)

We will discuss the exponential family of utility functions:

u(X) = \tau \exp(X / \tau) - 1)

Certainty Equivalent Value is an amount of guarantied payoff,
that agent considers similarly to the random one:

C^X(\tau) = u^{-1}(\mathbb{E}u(X)) = \tau \log \mathbb{E} \exp(X/\tau)

For exponential utility functions, certainty equivalent values are closely related to
the Cumulant Generative Function of a r.v.:

C^X(\tau) = \tau G^X(1/\tau)

K-learning

Variational Bayesian RL with Regret Bounds

LEMMA

The cumulant generating function of the posterior for the optimal Q-values satisfies the following Bellman inequality

C^{\star|t}_{s_l, a_l} \le \tilde G^{\mu|t}_{s_l, a_l}(1/\tau_t)\; +\; \sum_{s_{l+1}} \mathbb{E}^t ( P_{s_{l+1}, s_l, a_l}) \tau_t \log \sum_{a_{l+1}} \exp \big( C^{\star|t}_{s_{l+1}, a_{l+1}} / \tau_t \big)

or, similarly

C^{\star|t}_{l} \le \mathcal{B}(\tau_t, C^{\star|t}_{l+1})

where

\tilde G^{\mu|t}_{s_l, a_l}(\beta) = G^{\mu|t}_{s_l, a_l}(\beta) + \frac{(L-l)^2 \beta^2}{2(n^t_{s_l, a_l}+1)}

K-learning

Variational Bayesian RL with Regret Bounds

Let us find a function, which will satisfy the inequality with equality.

We will call it K-value:

K^t_{l} = \mathcal{B}(\tau_t, K^t_{l+1})

And we define policy as follows:

\pi(a_l|s_l) \propto \exp \big( K^t_{s_l, a_l} / \tau_t \big)

Then the regret is bounded:

K-learning

Variational Bayesian RL with Regret Bounds

Temperature at a fixed episode number can be found

as a solution of the following convex optimization problem:

K-learning

Variational Bayesian RL with Regret Bounds

For BANDITS

Deep Sea Environment 

K-learning

Variational Bayesian RL with Regret Bounds

Bsuite

More links to the God of links

References

Soft Q-learning:

https://arxiv.org/pdf/1702.08165.pdf

Soft Actor Critic:

https://arxiv.org/pdf/1801.01290.pdf

Big Review on Probabilistic Inference for RL:

https://arxiv.org/pdf/1805.00909.pdf

Implementation on TensorFlow:

https://github.com/rail-berkeley/softlearning

Implementation on Catalyst.RL:

https://github.com/catalyst-team/catalyst/tree/master/examples/rl_gym

Hierarchical policies (further reading):

https://arxiv.org/abs/1804.02808

Thank you for your attention!

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