Mathematical Analysis of Policy Gradient Methods

Most papers using reinforcement learning these days use the policy gradient class of learning. In this post, I will cover a basic tutorial of policy gradient, uncover some confusion on using baseline and regularization and give some suggestion for debugging these learning algorithms that I have found useful in the past.

Policy gradient methods is a class of reinforcement learning algorithm that optimize the reinforcement learning (RL) objective by performing gradient descent on the policy parameters. As opposed to value function based methods (SARSA, Q-learning), the central object of study in policy gradient methods is a policy function \pi(a\mid s; \theta) , which denotes a probability distribution over actions given a state. \theta are the parameters of this policy. Assuming we are optimizing using stochastic gradient methods our updates are given by:

 \theta^{(t + 1)} \leftarrow \theta^{(t)} +\eta_t \nabla_\theta J^\pi

The objective J^\pi can be expected total discounted reward, expected average reward or something else. We will stick to total discounted reward for this discussion. The aim now is to express \nabla_\theta J^\pi  in a form that is easier to work with. This is done using the policy gradient theorem.

Policy Gradient Theorem

Let M = \langle S, A, R, P, \gamma, \mu \rangle be our MDP where S, A, R, P are the state space, action space, reward function and transition probability respectively. \gamma is the discounting factor and \mu is an initial state distribution.

The expected total discounted reward can be expressed as:

J^\pi = E[\sum_{t\ge 0} \gamma^t r_t] = \sum_s \mu(s) V^\pi(s)

See my previous post if you don’t know what V function is. Using above equation we have \nabla_\theta J^\pi = \sum_s \mu(s) \nabla_\theta V^\pi(s). Policy gradient theorem (Sutton et al. 1999) computes V^\pi(s) by repeatedly applying the Bellman self consistency theorem. It really is just that.

\nabla_\theta V^\pi(s) =\nabla_\theta \left\{ \sum_a \pi(a\mid s) Q^\pi(s, a) \right\}

\nabla_\theta V^\pi(s) = \sum_a \pi(a \mid s) \nabla_\theta Q^\pi(s, a) + \sum_a Q^\pi(s, a)\nabla_\theta\pi(a \mid s) 

using Q^\pi(s, a) = \sum_{s'} P^a_{s, s'} \left\{ R^a_{s, s'} + \gamma V^\pi(s') \right\} we get:

\nabla_\theta Q^\pi(s, a) =  \gamma P^a_{s, s'} \sum_{s'} \nabla_\theta V^\pi(s')

so now we have an expression for \nabla_\theta V^\pi in terms of itself. Writing it down together we have:

\nabla_\theta V^\pi(s) = \sum_a Q^\pi(s, a)\nabla_\theta\pi(a \mid s)   +\sum_a \pi(a \mid s)\gamma P^a_{s, s'}  \sum_{s'}\nabla_\theta V^\pi(s')

we will change the notation as s \rightarrow s_0, a \rightarrow a_1, s' \rightarrow s_1 giving us:

\nabla_\theta V^\pi(s_0) = \sum_{a_1} Q^\pi(s_0, a_1)\nabla_\theta\pi(a_1 \mid s_0)   +\sum_{a_1} \pi(a_1 \mid s_0)\gamma P^{a_1}_{s_0, s_1}  \sum_{s_1}\nabla_\theta V^\pi(s_1).

unfolding this equation gives us (verify it for yourself):

\nabla_\theta V^\pi(s_0) =\sum_s \sum_{t \ge 0} \gamma^t P(s_t = s \mid \pi, s_0) \sum_a \nabla_\theta \pi(a \mid s) Q^\pi(s, a).

where P(s_t = s \mid \pi, s_0) is the probability of being in state s after time t when starting in state s_0 and taking actions according to the policy \pi. Note that our setup does not include multiple agents, policies dependent upon time or non-Markovian policies.

Discounted unnormalized visitation probability: The term \sum_{t \ge 0} \gamma^t P(s_t = s \mid \pi, s_0)  appears fairly often in RL theory so it is given a notation of d^\pi(s \mid s_0). Essentially this value is high for a state s if it is likely to be visited by an agent sampling actions according to the policy \pi and starting in state s_0. This term is called “discounted unnormalized visitation probability”. It is “visitation probability” since it is higher for state which are likely to be visited. It is discounted cause probability terms are discounted with \gamma^t factor and it is unnormalized cause it may not sum to 1. In fact for infinite horizon:

\sum_s d^\pi(s; s_0) = \sum_s \sum_{t \ge 0} \gamma^t P(s_t = s \mid \pi, s_0)

=  \sum_{t \ge 0} \gamma^t \sum_s P(s_t = s \mid \pi, s_0) = \sum_{t \ge 0} \gamma^t* 1 = \frac{1}{1-\gamma}.

and for finite horizon with \gamma =1 we have:

\sum_s d^\pi(s; s_0) = \sum_s \sum_{t \ge 0}^T P(s_t = s \mid \pi, s_0)

\sum_{t \ge 0}^{T-1} \sum_s P(s_t = s \mid \pi, s_0) = \sum_{t \ge 0}^{T-1} 1 = T

so in  either case the sum of d^\pi(s; s_0 is equal to the time horizon (for episodic undiscounted case) or effective time horizon (for unending discounted case).

using the notation we have: \nabla_\theta V^\pi(s_0) = \sum_s d^pi(s; s_0) \sum_a \nabla_\theta \pi(a \mid s) Q^\pi(s, a)

and which gives us:

\nabla_\theta J^\pi = \sum_{s_0} \mu(s_0) \sum_s d^pi(s; s_0) \sum_a \nabla_\theta \pi(a \mid s) Q^\pi(s, a)

or using the notation of Kakade and Langford 2002, we define d^\pi_\mu(s; s_0) = \sum_{s_0} \mu(s_0) d^\pi(s; s_0) which allows us to express the gradient as:

\nabla_\theta J^\pi = \sum_s d^\pi_\mu(s) \sum_a \nabla_\theta \pi(a \mid s) Q^\pi(s, a).

Another way to express objective: Using the notation of visitation distribution, allows us to express the expected total discounted reward objective in a form that gives another interpretation to the policy gradient theorem. Observe that:

J = E[\sum_{t \ge 0} \gamma^t r_t] = \sum_{t \ge 0} \gamma^t E[r_t]  (linearity of expectation)

reward r_t = R(s_t, a_{t+1}) where s_t, a_{t+1} is the state in which the agent is at time t and a_{t+1} is the action taken at that time. Therefore:

E[r_t] = E[R(s_t, a_{t+1})] = \sum_{s} P(s_t = s) \sum_a \pi(a \mid s).

Plugging it in we get:

J^\pi = \sum_{t \ge 0} \gamma^t E[r_t] =\sum_{t \ge 0} \gamma^t\sum_{s} P(s_t = s) \sum_a \pi(a \mid s).

Rearranging the terms we get:

J^\pi = \sum_{t \ge 0} \gamma^t E[r_t] =\sum_{s} P(s_t = s) \sum_{t \ge 0} \gamma^t\sum_{s} P(s_{t-1} = s) \sum_a \pi(a \mid s)

and then using the notation of d^\pi_\mu(s) we get:

J^\pi = \sum_s d^\pi_\mu(s) \sum_a \pi(a \mid s) R(s, a).

Compare this with the gradient of the objective:

\nabla J^\pi = \sum_s d^\pi_\mu(s) \sum_a \left\{ \nabla \pi(a \mid s) \right \}Q^\pi(s, a).

It appears as if we don’t take gradient over the term d^\pi_\mu(s) and it comes with the price of replacing R(s, a) with Q^\pi(s, a). It also allows us to derive a corollary for derivative of visitation distribution. Derivative of visitation distribution has been studied in other context, not using this corollary, which I will probably cover in a future blog. 

Corollary*: \sum_s \nabla_\theta d^\pi_\mu(s) \sum_a \pi(a \mid s) R(s, a) =\sum_s d^\pi_\mu(s) \sum_a \left\{ \nabla \pi(a \mid s) \right \}\{ Q^\pi(s, a) - R(s ,a)\}

Proof: \nabla J^\pi = \sum_s d^\pi_\mu(s) \sum_a \left\{ \nabla \pi(a \mid s) \right \}Q^\pi(s, a) (from policy gradient theorem).

\nabla J^\pi = \nabla \left( \sum_s d^\pi_\mu(s) \sum_a \pi(a \mid s) R(s, a) \right) (from alternative expression for J^\pi).

=  \sum_s \nabla d^\pi_\mu(s) \sum_a \pi(a \mid s) R(s, a) + \sum_s  d^\pi_\mu(s) \sum_a \nabla \pi(a \mid s) R(s, a)

comparing the two equations and rearranging gives the result.

The REINFORCE algorithm

REINFORCE algorithm (Williams 1992) comes directly from approximating the gradient given by the policy gradient objective. Firstly, we will review sampling for the wider audience.

Brief Summary on Sampling: Given an expectation E_p[f] = \sum_s f(s) p(s) of a function f: X \rightarrow \mathbb{R} with respect to probability distribution p over X, we can approximate the expectation by drawing N samples x_i \sim p(.) and using empirical mean given by \mu_N = \sum_{i=1}^N \frac{f(x_i)}{N} as our approximation. From the law of large number this approximation will converge to E_p[f] as N \rightarrow \infty. The estimate \mu_N is called unbiased since its mean is the quantity we are approximating i.e. E_p[f]. E_{x_i \sim p}[\sum_{i=1}^N \frac{f(x_i)}{N}] = \sum_{i=1}^N \frac{1}{N} E_{x_i \sim p}[f(x_i)] =\sum_{i=1}^N \frac{1}{N} E_p[f] = E_p[f]. Note that if our samples were from another distribution then our estimate may not be unbiased. Another quantity of interest for an estimate is its variance given by var(X) = E[(X- E[X])^2], which measures how far can the estimate deviate on expectation from the mean. For the above estimate, we have var(\sum_{i=1}^N \frac{1}{N}E_{x_i \sim p}[f(x_i)]) = \frac{var(x)}{N}. Thus, as we collect more samples our estimate deviates less from the mean and if the variance var(x) is low then even fewer samples can help us get accurate estimate.

Vanilla REINFORCE: A quick look at the policy gradient tells us that we can never compute it exactly except for tiny MDP. We therefore want to approximate the gradient of J^\pi given by policy gradient objective and we will do it using sampling. We show below how to do it,

\nabla J^\pi = \sum_s d^\pi_\mu(s) \sum_a \left\{ \nabla \pi(a \mid s) \right \}Q^\pi(s, a)

=\sum_s \sum_{t\ge 0} \gamma^t P(s_t = s \mid \pi) \sum_a \left\{ \nabla \pi(a \mid s) \right \}Q^\pi(s, a)

\sum_{t\ge 0} \gamma^t \sum_s P(s_t = s \mid \pi) \sum_a \left\{ \nabla \pi(a \mid s) \right \}Q^\pi(s, a)

Let’s say we sample a rollout \langle s_0, a_1, r_1, s_1, a_2, r_2, \cdots a_T, r_T, s_T \rangle using the policy \pi. A rollout is generated by starting from a sampled state s_0 \sim \mu(.) and then taking actions in the current state using the policy and receiving reward. Then note that s_t \sim P(. \mid \pi ). Then using the sampling approach described above,

\sum_s P(s_t = s \mid \pi) \sum_a \left\{ \nabla \pi(a \mid s) \right \}Q^\pi(s, a) \approx  \sum_a \left\{ \nabla \pi(a \mid s_t) \right \}Q^\pi(s_t, a)

Note that we only use a single sample here (N=1) for approximation!

After this approximation we have,

\nabla  J^\pi \approx \sum_{t\ge 0} \gamma^t \sum_a \left\{ \nabla \pi(a \mid s_t) \right \}Q^\pi(s_t, a)

If we knew Q^\pi(s, a) perfectly for every state s and action a then we can compute the above objective perfectly provided the number of actions are not prohibitively large. However, in general we do not have these conditions therefore we want to get ride of the second summation as well.

Unfortunately, the summation is not written in the form of an expectation i.e. \sum_a f(a) p(a) for some probability distribution p. Therefore we cannot apply the sampling approach. To solve this, we will use calculus 101 to express it in an expectation format by using \sum_a (\nabla p(a)) f(a) = \sum_a p(a)  (\nabla \ln p(a) )f(a) = E_{a \sim p(.)}[ \nabla \ln p(a) f(a)]. Thus,

\nabla  J^\pi \approx \sum_{t\ge 0} \gamma^t \sum_a \left\{\pi(a \mid s_t) \nabla \ln \pi(a \mid s_t) \right \}Q^\pi(s_t, a)

We already have samples from \pi(. \mid s_t) which are nothing but the action a_{t+1}. Using it as a sample gives us:

\nabla  J^\pi \approx \sum_{t \ge 0} \gamma^t \nabla \ln \pi(a_{t+1} \mid s_t) Q^\pi(s_t, a_{t+1}).

We are still left with estimating Q^\pi(s_t, a_{t+1}) to complete our algorithm. Turns out, we can approximate even this by sampling. By definition, Q^\pi(s, a) = E[\sum_{t \ge 0} \gamma^t r_t \mid s_0=s, a_1=a] is an expectation of total discounted reward collected by a rollout sampled using our policy. For Q^\pi(s_t, a_{t+1}), the value \sum_{t' \ge t} \gamma^{t'-t} r_{t'+1} is an unbiased sample estimating Q^\pi(s_t, a_{t+1}) (convince yourself that this is true). This gives us

\begin{aligned}  \nabla  J^\pi &\approx \sum_{t \ge 0} \gamma^t \nabla \ln \pi(a_{t+1} \mid s_t)\sum_{t' \ge t} \gamma^{t'-t} r_{t'+1} \\  \nabla  J^\pi &\approx \sum_{t \ge 0} \nabla \ln \pi(a_{t+1} \mid s_t)\sum_{t' \ge t} \gamma^{t'} r_{t'+1}  \end{aligned}  .

After these approximations, we are ready to state the vanilla REINFORCE algorithm:

The REINFORCE Algorithm

  1. Initialize the parameters \theta^0 randomly.
  2. Generate rollout s_0, a_1, r_1, s_1, \cdots a_T, r_T, s_T using the current policy \pi_\theta
  3. Do gradient ascent: \theta^{t+1} \leftarrow \theta^t + \sum_{t \ge 0}  \nabla \ln \pi(a_{t+1} \mid s_t) \sum_{t' \ge t} \gamma^{t'} r_{t'+1}

Keep the Samples Unbiased. Throw away Rollouts after Update.

Our entire derivation approximating the value of J^\pi relies on a single rollout that is generated using the policy \pi. If the rollout was generated using another policy then our assumptions will not be valid anymore. This is true, if we keep old rollouts and update the policy. Then those rollouts are no longer unbiased sample of the current policy and hence do not estimate the objective above. Therefore, we cannot keep those samples after update. This is unlike Q-learning where experiences are stored in huge replay memory. Since our derivation relies on samples coming from the current policy, therefore REINFORCE is an example of on-policy reinforcement learning approach. It seems kind of wasteful and inefficient to throw away old samples after updating the parameter. A common tactic is therefore to collect several rollouts in parallel using large number of threads. We will talk about this in another blog.

Problem with REINFORCE:

REINFORCE is not a very good learning algorithm for anything interesting. Few main issues with it are:

  1. No Exogenous Randomization: REINFORCE uses the same policy for exploration as the one being optimized. There is no way to control the flow of exploration or way to add exogenous randomization. This can hinder exploration since the policy can get stuck in specific regions and may not be able to explore outside.
  2. Variance: We used a single rollout above to make several approximations. This can affect the variance of our estimate of the gradient. Having high-variance estimate can prohibit learning.
  3. Degenerate Solutions: REINFORCE can get stuck in degenerate solutions where the policy can suddenly become deterministic while being far from optimal. Once the policy is deterministic, the updates stop since our estimated gradient:

\sum_{t \ge 0}  \nabla \ln \pi(a_{t+1} \mid s_t) \sum_{t' \ge t} \gamma^{t'} r_{t'+1},

becomes 0. This effectively kills the learning and it has to be restarted. This situation is frequently encountered in practice and we will call it the entropy-collapse issue. Q-learning does not suffer from this degeneracy.

Next we will discuss some solutions to above problem and discuss few methods for debugging.

No Exogenous Randomization

REINFORCE’s inability to separate the exploration policy from the policy being optimized is one of its biggest weakness. There are atleast two different ways in which this has been addressed. The first approach uses a warm start to initialize the policy using behaviour cloning or imitation learning. This also makes sense from deployment point of view where we have to place our agent in the real world in order to perform reinforcement learning and thus don’t want the start policy to be randomly initialized (imagine asking users to chat with a randomly intialized conversation model!). One can naively hope that warm start will enable the policy to explore in the right region of the problem.

The other approach is to use a separate exploration policy and unbias the gradients by using importance weights. However this approach may not be suitable if the policy has close to 0 mass on actions which are chosen by the exploration policy.

Another line of research involves designing intrinsic reward function (pseudo-counts, prediction error, variance of ensemble) to incentivize the policy to eventually learn to explore the desired state space. However, these approaches generally range from having theoretical guarantees in tabular MDPs (e.g., MBIE-EB count based exploration) to having no theoretical guarantee at all (e.g., prediction error).

Reducing Variance through Control Variate

We made a remark earlier pertaining to the variance of the estimate of the gradient for the REINFORCE algorithm. Let’s make it more formalized:

Given a rollout, \tau = \langle s_0, a_1, r_1, s_1, \cdots a_T, r_T, s_T \rangle generated by a policy \pi, we approximate the derivative of expected total discounted reward \nabla J^\pi using:

X(\tau) = \sum_{t \ge 0} \nabla \ln \pi(a_{t+1} \mid s_t)\sum_{t' \ge t} \gamma^{t'} r_{t'+1}

This estimate is unbiased i.e. E[X(\tau)] = \nabla J^\pi and its variance is given by:

Var(X) = E[(X(\tau) -\nabla J) (X(\tau) - \nabla J)^T]. This variance can be high due to  the value of \sum_{t' \ge t} \gamma^{t'} r_{t'+1}. Therefore, we want to modify the estimate, keeping it unbiased but reducing the variance. In statistics, one way to do this is to use control-variate.

Control-Variate: Let X be an estimate of a quantity with mean \mu, we define a control variable Z with mean E[Z] and define a new estimate given by Y = X + c (Z - E[Z]). This new estimate is still unbiased as E[Y] = E[X] + c E[Z] - c E[Z] = \mu. The variance is given by:

Var(Y) = Var(X) + c^2 Var(Z) + 2c Cov(X, Z) which can be made smaller than Var(X) by optimizing the choice of c and Z.

Reinforce Baseline: We need to define a control variable to reduce variance.

We define it as: Z(\tau) = \sum_{t \ge 0} \gamma^t \nabla \ln \pi(a_{t+1} \mid s_t) b(s_t) ,  where b: \mathcal{S} \rightarrow R is a function called the baseline. We can observe that E[Z(\tau)] = 0 as,

E[Z(\tau)] = E[\sum_{t \ge 0} \gamma^t \nabla \ln \pi(a_{t+1} \mid s_t) b(s_t) ]

E[Z(\tau)] = \sum_{t \ge 0} \gamma^t E[\nabla \ln \pi(a_{t+1} \mid s_t) b(s_t) ]

=  \sum_{t \ge 0} \gamma^t \sum_{s_t} P_t(s_t) \sum_{a_{t+1}} \pi(a_{t+1} \mid s_t) \nabla \ln \pi(a_{t+1} \mid s_t) b(s_t)

= \sum_{t \ge 0} \sum_{s_t} P_t(s_t) b(s_t) \sum_{a_{t+1}} \nabla \pi(a_{t+1} \mid s_t)

= \sum_{t \ge 0} \sum_{s_t} P_t(s_t) b(s_t) \nabla \sum_{a_{t+1}} \pi(a_{t+1} \mid s_t)

=  \sum_{t \ge 0} \sum_{s_t} P_t(s_t) b(s_t) \nabla 1 = 0.

We will further set the value of c=-1, giving us our new estimate as:

Y(\tau) = X(\tau) - (Z(\tau) - E[Z]) =\sum_{t \ge 0} \gamma^t \nabla \ln \pi(a_{t+1} \mid s_t) \{ Q(s_t, a_{t+1}) - b(s_t) \}, where Q(s_t, a_{t+1}) = \sum_{t' \ge t} \gamma^{t' - t} r_{t' + 1}.

Proof of Variance Reduction(*):  We still haven’t chosen a baseline function and we set the value of c = -1 without justification. Overall we still haven’t shown that variance gets reduced. I will provide a proof that gives a choice of the baseline function and proves variance reduction. For simplicity, we will consider a single step case (i.e. \tau = \langle s_0, a_1, r_1, s_1 \rangle . We have,

Var(Y) = Var(X) + Var(Z) - 2 Cov(X, Z)

= Var(X) + E[(Z - E[Z])^2] - 2 E[(X- E[X])(Z-E[Z])]

= Var(X) + E[Z^2] - 2 E[XZ] + 2 E[X] E[Z] = Var(X) + E[Z(Z - 2X)].

Where E[Z(Z-2X)] = E[ \nabla p(a_1 \mid s_0) b(s_0)  \{\nabla p(a_1 \mid s_0) \{ b(s_0) - Q(s_1, a_1)\} \}].

which can be simplified to = E[  \|\nabla p(a_1 \mid s_0) \|^2 b(s_0) \{ b(s_0) - Q(s_0, a_1) \}]

= E[\|\nabla p(a_1 \mid s_0) \|^2] b^2(s_0) - b(s_0) E[ \|\nabla p(a_1 \mid s_0) \|^2 Q(s_0, a_1)].

setting it to 0 gives the optimal baseline as b(s_0) = \frac{E[ \|\nabla p(a_1 \mid s_0) \|^2 Q(s_0, a_1)]}{E[\|\nabla p(a_1 \mid s_0) \|^2]}.

If one makes an independency assumption between \|\nabla p(a_1 \mid s_0) \|^2 and Q(s_0, a_1) then we get

b(s_0) = \frac{E[ \|\nabla p(a_1 \mid s_0) \|^2 Q(s_0, a_1)]}{E[\|\nabla p(a_1 \mid s_0) \|^2]} =\frac{E[ \|\nabla p(a_1 \mid s_0) \|^2] E[Q(s_0, a_1)]}{E[\|\nabla p(a_1 \mid s_0) \|^2]} =E[Q(s_0, a_1)] = V(s_0).

One can do a similar analysis for multi-step reinforcement learning and derive the optimal policy. While the optimal policy has been known for many years, all empirical application of REINFORCE or its derivative (in which I count actor-critic methods) use an  approximated baseline given by the state-value function V^\pi. I am not familiar with any literature where they prove (or disprove) if the approximate baseline actually does reduces the variance. However, in the empirical RL circle the variance reduction due to the approximated baseline is often taken as granted.

Importance of Regularization

REINFORCE can get stuck in degenerate solutions as pointed out before. To avoid this degeneracy, a common tactic of regularizing the objective with the entropy of the policy or KL-divergence from a reference policy is adopted. We will focus on the former method here. The entropy regularized objective is given below:

J^\pi_\lambda = \sum_s d^\pi_\mu(s) \left\{ \sum_a \pi(a \mid s) R(s, a) + \lambda H(\pi(.\mid s)) \right\},

where \lambda is a hyperparameter controlling the effect of the regularization. If one computes the derivative of the new objective one gets:

\nabla J^\pi_\lambda = \sum_s d^\pi_\mu(s) \sum_a \nabla \pi(a \mid s) Q^\pi(s, a) + \lambda \sum_s \nabla \{d^\pi_\mu(s)  H(\pi(.\mid s))\}

Most researchers however use biased gradients given below:

\nabla J^\pi_\lambda = \sum_s d^\pi_\mu(s) \sum_a \nabla \pi(a \mid s) Q^\pi(s, a) + \lambda \sum_s d^\pi_\mu(s)  \nabla H(\pi(.\mid s))

\Rightarrow \nabla J^\pi_\lambda = \sum_s d^\pi_\mu(s) \{ \sum_a \nabla \pi(a \mid s) Q^\pi(s, a) + \lambda H(\pi(.\mid s))\} .

Entropy regularized objective will no longer follow our classic Bellman optimality conditions and the optimal policy no longer remains deterministic. An easy verification of this is to set \lambda = \infty and observe that the optimal solution of \arg\max_\pi J^\pi_\lambda will be to remain uniformly random everywhere (assuming the reward function is bounded).

Evaluating Training Progress

When training a classifier for a task like ImageNet, one generally monitors the training error and the error on a held out tune set. The decrease in training error tells that your model has sufficient capacity and an increase in tune set indicates potential overfitting. Consider, the approximated gradient of the objective for REINFORCE:

\nabla  J^\pi \approx \sum_{t \ge 0} \nabla \ln \pi(a_{t+1} \mid s_t)\sum_{t' \ge t} \gamma^{t'} r_{t'+1}

the standard way to program this using pytorch or tensorflow is to define the loss variable:

L = -\sum_{t \ge 0} \ln \pi(a_{t+1} \mid s_t)\sum_{t' \ge t} \gamma^{t'} r_{t'+1}

when doing gradient descent on this variable we get the same gradient as the approximation gives us. This does not make L the real loss function, in fact it is not the loss for the REINFORCE algorithm at all but using it gives us the real gradients therefore we will call it the substitute loss.

Monitoring the substitute loss is not the same as monitoring the actual training loss when doing supervised learning. To begin with, notice that the substitute loss is positive when the agent receives only positive reward and it is negative when the agent only receives negative rewards. This is counterintuitive as one ideally associates high loss with low return. So why is this so?

This happens cause when all the rewards are positive then the loss is positive and the only way the agent can reduce it is by pushing the term \ln \pi(a_{t+1} \mid s_t) towards 0 which means increasing the probability of actions which generate positive reward (which is what we want). Similarly, when all rewards are negative then the loss is negative and the only way the agent can make it more negative is by decreasing the probability of these actions until the term \ln \pi(a_{t+1} \mid s_t) tends towards negative infinity. This will eventually lead to these actions not being sampled anymore.

Thus, instead of monitoring the substitute loss, one can monitor two things: (i) the total reward received by the agent which also represents an unbiased estimate of the actual objective and (ii) the entropy of the policy. Ideally the total reward achieved by the agent should increase and the entropy of the policy should decrease. Any stagnation means the learning is not happening effectively.

Major Failure Case for Policy Gradient

Policy gradient methods have no guarantees in theory or practice. A simple example which can be used to demonstrate this is from John Langford. There are H+1 states and two actions and the agent always starts in state s_0. At each state s_i (i \in \{0, 1, \cdots, H-1\}), an action takes you to s_{i+1} and the other actions takes you back to s_0. The action mappings are randomly assigned to each state but the MDP is deterministic. Every state has a reward of 0 except s_H which has a reward of 1. The game ends when the agent reaches the state s_H.

combolock.001.jpeg

With a close to 1 probability, any rollout will fail to reach the destination state where it earns a reward and therefore will not cause any meaningful learning when using on-policy policy gradient methods. It will require exponentially many samples for it to reach the state s_H and therefore on-policy policy gradient with a randomly initialized policy is not what is called a  PAC RL algorithm, which is an important theoretical guarantee.

Conclusion

Despite its flaws policy gradient methods are used widely for all sort of AI applications. One reason behind its widespread use is their remarkable similary to supervised learning with which most empiricists are widely familiar. Thus, one can train their favourite dialogue model using REINFORCE by simply multiplifying the log probabilities with a value term. Further, variants of policy gradient methods like PPO perform much better and are reasonable baselines. However, it could also be the case that most people don’t care about guarantees as long as they can solve their favourite childhood game and as a field we must guard against this trend as we move towards more serious applications of these algorithms.

 

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Is the MTA System Holding New York City Back?

I presume that the title of this blog would come across as either shocking or trivial. Growing up in a 2nd tier city in India, I always imagined New York City to be the center of civilization for our time. Though I never gave much consideration to its transport system, my mind occupied with its lavish skyscrapers, busy life and a broad range of restaurants all accessible in a tiny island which generates more money than most countries; I would have imagined it in positive terms. However, having spent several years in the city I have come to accept the current state of the MTA system as serious impediment to the city’s long term competitiveness. At best it is annoying and at worst it can cause serious harm. An assortment of news articles and research show that New Yorkers have lost money, jobs and precious time. For my own part, I have missed (been late for) many meetings, classes, dates and other appointments. This tardiness manifested in several form. At times the train skipped my destination, other times it metamorphosed in the middle of the journey, local became express, express became local, laws of relativity changed to mean 2 mins to the next train to imply 20 mins. However, two incidents were particularly dreadful. In both instances, the train stopped in the middle of a tunnel for almost  2hours without moving, wifi or phone signal and often with extremely uncomfortable coach temperatures. While I was able to grit my teeth, I can only imagine the condition of infants, pregnant women, elderly or people with health issues (imagine someone with bladder issues). The most horrific incident that I know to be true, can be read here.

After one such incident described above, my Swedish friend educated me on the policy of his country which entitles passengers to reimbursement if they end up experiencing delay in commute. A quick conversation with my fellow subway passengers reveals a deep cynicism towards the possibility of this scheme in New York. This is depressing considering that GDP of New York metropolitan area is about twice the national economy of Sweden. My girlfriend, who is a sociologist, tells me that part of the reason is due to unwillingness of state officials in Albany to spend more money in improving the system.

Back in my home country, the metro system in the capital New Delhi is not only much cleaner and better but also arguably more environment friendly. I was equally impressed with the metro system in Beijing which is better despite being longer and busier than New York city. Only my experience with the Paris metro system was unimpressive. However, the case is not as simple as poorer countries doing better due to their adoption of the recent technology since both subway systems in Berlin and in Tokyo are considered excellent. Maybe the commute of passengers means more to these countries and their officials?

As the economy of other countries rise and other cities continue to grow, as someone who has come to deeply love New York, I would be disappointed if the city loses the baton of the center of civilization.

Learning to Listen: Acquiring a New Language vs. Doing Sociology with One’s Ears

Sociological Reasoning

I have mentioned in various blog posts that Hindi is the language that I am learning this year (Than 2017a, Than 2017b). My current level is A1 according to the Common European Framework of Reference for Languages. My goal is to move to the next level by the end of April. Before starting my Hindi language learning journey I messaged a couple of polyglot friends, and they sent me materials to self-learn the language. Yet to be honest, I am nowhere at the level where I can teach myself a language that I have had very little contact with. Therefore, looking for a language teacher online was my solution. I found one from Mumbai. Rachna Singh is her name. She is very patient, and positive all the time.  We have had regular lessons for almost two months.

My Hindi learning routine goes as follows: 5 minutes…

View original post 1,153 more words

Markov Decision Process and Reinforcement Learning (Part 1)

Markov decision process (MDP) is a framework for modeling decision making and is very popularly used in the areas of machine learning, robotics, economics and others. An MDP models a decision maker (henceforth agent) in a world, where the agent gets a reward for performing action and the long term goal is to maximize the total reward. The concept of MDP is very intimately tied with the idea of Reinforcement Learning (RL), which is a machine learning framework for learning policies for decision making.

The general idea of MDP has been around since 1950s whereas RL has been around since 1969 (Andreae’s 1969). Long considered (often unjustifiably) suitable for only toy problems, RL has seen a recent renewal due to reasons, I will explain later. There is a good amount of literature on MDP and RL, with the Reinforcement learning book by Sutton and Barto, being a particularly good introduction to the subject. However, I found that these resources remove some essential mathematical treatment or they are written for mathematicians and beyond the reach of general audience.

In this post, I will cover a simple mathematical treatment of MDP with proofs of fundamental concepts. It will be best to use this post as complementary to Sutton and Barto. At places, I did not find proof of many theorems, and in such cases I have supplied my own proofs (marked with *). Please be careful in reading these proofs and let me know if you find any error.

1. Introduction

Definition: An MDP M is defined by the tuple (S, A, P, R, \gamma) where S is a set of states, A is a set of actions that an agent can take, P^a_{s,s'} is defined for all s,s' \in S and a \in A and denotes the probability of transitioning to state s' from state s on taking action a, we will denote such a transition as s \rightarrow_a s'R^a_{s,s'} is a real number denoting the reward received by the agent in the transitions \rightarrow_a s'\gamma is a real number that denotes how much to discount future rewards (its purpose will become clear shortly).

Thus, MDP defines a problem setup, where there is an agent that can take action and in the process receive a reward as well change the state. The setup is Markovian since the next state and reward depends only upon the current state and the action taken.

The goal of a rational agent should be to achieve the maximum reward in the long term. This is expressed as a policy which gives a distribution over actions to take, when presented with a state. More formally, 

Definition: Policy \pi: S\times A \rightarrow [0,1] defines the probability distribution over action given a state. \pi(s,a) denotes the probability of taking action a given state s. The policy is deterministic if \forall s\in S, \exists a \in A \,\,s.t.\,\, \pi(s,a) = 1 .

The agent then plays a game where it is landed in a state s, and it continuously takes action to maximize the total reward. More precisely, a trace \tau of agent’s game will look like a sequence of state, action, reward \tau = , where a_t is the action taken when presented with state s_t and r_t is the reward given in the process and s_{t+1} is the new state.

What we are interested in is the expected reward given the states, which we denote by the concept of state value function V^\pi(s).

 V^\pi(s) = E[ \sum_{t=0}^\infty \gamma^{t} r_{t + 1} | s_o = s] 

a similar concept is given by action value function Q^\pi(s,a) :

 Q^\pi(s) = E[ \sum_{t=0}^\infty \gamma^{t} r_{t + 1} | s_o = s, a_1 = a]

Episodic and Continuous Task: If the task defined by MDP, always terminates in  some bounded number of steps irrespective of the starting state and actions taken by the agent, the task is called episodic (will game of Chess be episodic? how about a racing game?). A single play in an episodic task is called an episode. If a task is not episodic then it can potentially run forever and such tasks are called continuous task.

Discounting: As seen in the equation above, the reward at time step t+1 is discounted by \gamma^t. There are several reasons why it is done but two main reasons are: there are evidence that humans behave in similar manner and secondly, we want the value function is a well defined quantity. When the reward is bounded by some finite constantM and \gamma \in [0, 1) then value function is always defined. The maximum reward achievable is bounded by \frac{M}{1-\gamma} as shown below:

 V^\pi(s) =E[ \sum_{t=0}^\infty \gamma^{t} r_{t + 1} | s_o = s]

 V^\pi(s) \le E[ \sum_{t=0}^\infty \gamma^{t} M | s_o = s] = M \sum_{t=0}^\infty  \gamma^t = \frac{M}{1-\gamma}

Note that the maximum achievable reward is M * \frac{1}{1-\gamma}. If the task was episodic and terminates in T steps, then the maximum achievable reward in undiscounted scenario would have been MT. Comparing it with what we have for continuous discounted task, we see that \frac{1}{1-\gamma} plays the role of T and for this reason, this is sometimes called the effective time horizon of the task. If \gamma = 0.99 means that the task is effectively being played for 100 steps.

In our proofs below, we will focus only on continuous tasks with \gamma \in [0,1).

2. Bellman Self-consistency equation

2.1 Self-consistency equation

 By a little work, we can express the value functions in recursive form giving us what is called the self-consistency equation. Lets see how it is done:

 V^\pi(s) = E[ \sum_{t=0}^\infty \gamma^{t} r_{t + 1} | s_o = s]  = \sum_{\tau} \left(\sum_{t=0}^\infty \gamma^{t} r_{t + 1}\right) p(\tau)     = \sum_{a_1, r_1, s_1} \sum_{\tau'} \left(\sum_{t=0}^\infty \gamma^{t} r_{t + 1}\right) p(\langle a_1, r_1, s_1\rangle:\tau')  = \sum_{a_1, r_1, s_1} \sum_{\tau'}\left( r_ 1 + \sum_{t=1}^\infty \gamma^{t} r_{t + 1}  \right) p(\langle a_1, r_1, s_1\rangle)p(\tau' | s_1)   = \sum_{a_1, r_1, s_1}p(\langle a_1, r_1, s_1\rangle)\sum_{\tau'}\left\{ r_1  +\left( \sum_{t=1}^\infty \gamma^{t} r_{t + 1}  \right) \right\}p(\tau' | s_1)   = \sum_{a_1, r_1, s_1}p(\langle a_1, r_1, s_1\rangle)\left\{\sum_{\tau'}r_1p(\tau' | s_1)  + \sum_{\tau'}\left( \sum_{t=1}^\infty \gamma^{t} r_{t + 1}  \right)p(\tau' | s_1) \right\}

  r_1  only depends upon  s, a_1, s_1 therefore we get

  = \sum_{a_1, r_1, s_1}p(\langle a_1, r_1, s_1\rangle)\left\{r_1  + \gamma \sum_{\tau'}\left( \sum_{t=0}^\infty \gamma^{t} r_{t + 2}  \right)p(\tau' | s_1) \right\}

the second term is same as V^\pi(s_1) therefore we get

  = \sum_{a_1, r_1, s_1}p(\langle a_1, r_1, s_1\rangle)\left\{r_1  + \gamma V^\pi(s_1) \right\}

Expanding p(\langle a_1, r_1, s_1 \rangle) using the fact that given s_1, a_1 the reward r_1 is fixed, we get:

V^\pi(s) = \sum_{a_1} \pi(s, a_1) \sum_{s_1} P^a_{s, s_1}\left\{R^a_{s,s_1}  + \gamma V^\pi(s_1) \right\}  

this is the Bellman self consistency equation for the value function. Basically any state-value function should satisfy the self-consistency equation.

Similarly, Q^\pi satisfies a self-consistency equation below:

Q^\pi(s,a) = \sum_{s'}P^a_{s,s'} \{ R^a_{s,s'} + \gamma \sum_{a'} \pi(s',a')Q^\pi(s', a') \}

we also have relation between Q^\pi and V^\pi given by:

Q^\pi(s,a) = \sum_{s'} P^a_{s,s'} \{ R^a_{s,s'} + \gamma V^\pi(s') \}

It is not clear whether the self-consistency equations admit a unique solution. We show below that infact they do for finite MDPs.

2.2 Self-consistency equation has a unique solution

Theorem*: Self-consistency equation for state-value function for finite MDPs have a unique solution.

Proof: We have  \forall s\in S  

V^\pi(s) = \sum_{a} \pi(s, a) \sum_{s_1} P^a_{s, s_1}\left\{R^a_{s,s_1}  + \gamma V^\pi(s_1) \right\}   V^\pi(s) = \sum_{s_1} \{ \gamma \sum_{a}\pi(s, a)P^a_{s, s_1} \} V^\pi(s_1) + \sum_{s_1} \sum_{a} \pi(s,a) P^a_{s,s_1} R^a_{s,s_1}  

define \alpha_{s,s_1} =\{ \gamma \sum_{a}\pi(s, a) P^a_{s, s_1} \}, and \beta_s =  \sum_{s_1} \sum_{a} \pi(s,a) P^a_{s,s_1} R^a_{s,s_1}  we get

V^\pi(s) = \sum_{s_1}\alpha_{s,s_1} V^\pi(s_1) + \beta_s   

or in matrix form with  A = [\alpha_{s,s_1}] and b = [\beta_s],

I V^\pi = A V^\pi + b   \Rightarrow (I - A) V^\pi = b

We show that this linear equation has a unique solution by showing that matrix  (I - A) has a null space of dimenion 0.

Let  x = (x_s) \in N(I - A) \Rightarrow (I - A) x = 0  or equivalentlty  Ax = x .

 x_s = \sum_{s'} \alpha_{s,s'} x_{s'}

Here we note following properties of matrix  A :

  1.  \alpha_{s,s'} \ge 0
  2.  \sum_{s'} \alpha_{s,s'}  = \gamma \sum_{s'} \sum_{a}\pi(s, a)P^a_{s, s'}
    =\gamma \sum_{a}\pi(s, a)(\sum_{s'} P^a_{s, s'}) =  \gamma \sum_{a}\pi(s, a) = \gamma .

Defining  x_{max} = max\{x_s | s\in S\} and  x_{min} = min\{x_s | s\in S\} and using the above property of matrix  A we get.

This means  \gamma x_{min} \le x_s = \sum_{s'} \alpha_{s,s'} x_{s'} \le \gamma x_{max} . This implies:

 \gamma x_{min} \le x_{min}  \Rightarrow (1 - \gamma) x_{min} \ge 0 \Rightarrow x_{min} \ge 0 and
 x_{max} \le \gamma x_{max} \Rightarrow (1 - \gamma) x_{max} \le 0 \Rightarrow x_{max} \le 0

Using  x_{min} \ge 0 and  x_{max} \le 0 , we get  x = 0 . This implies that  N(I - A) = \{ 0 \} and therefore the self consistency equation for state-value has a unique solution.

3. Optimal Policy

A natural question to ask is, how to find a policy that gives us very high value of state value function. For a given state s, we can say that policy \pi is as good as   \pi^{'} if V^\pi(s) \ge V^{\pi'}(s). The policy is better if the inequality is strict. It is easy to see that for finite MDP and state, there exists a policy that is as good as all other policies. But how do we define an ordering over policies, irrespective of state?

One way would be, \pi is as good as   \pi^{'} if \forall s\in S; V^\pi(s) \ge V^{\pi'}(s). That is the policy must equal (or exceed) the other policy for every state. But this leads to the question of optimality, does an optimal policy exists. After all, we can end up in a situation where for two states s_1, s_2 \in S and policies \pi_1, \pi_2; V^{\pi_1}(s_1) > V^{\pi_2}(s_1)  and V^{\pi_1}(s_2) < V^{\pi_2}(s_2). We now prove that such an optimal policy does exists for finite MDP.

3.1 Existence of optimal policy

Theorem*: There exists an optimal policy for every finite MDP.

Proof:  Let MDP be M = (S,A,P,R,\gamma) be a finite MDP and \Pi be the space of all possible policies \pi. For a given state s, \exists \pi_s \in \Pi such that V^{\pi_s}(s) \ge V^{\pi}(s) \forall \pi \in \Pi. We call such \pi_s as the leader policy for state s. We now define a policy as \pi(s,a) = \pi_s(s,a) i.e. for a given state $s$ we use the leader policy for that state to make our move.

This seems most natural and probably the only way to define \pi since, for a given state s the maximum reward is attained by policy \pi_s and hence if we do not follow the policy \pi_s for the first step itself then we may not be guaranteed to achieve the maximum reward in the long run. All that remains to show is that \pi(s,a) is optimal. For this we first define \delta(s) = V^\pi(s) - V{^\pi_s}(s). Showing that \pi is optimal corresponds to showing that \delta(s) \ge 0 \forall s\in S since if we beat leader policy for every state then we beat all policies for all states.

From Bellman self-consistency equation we have:

V^{\pi}(s) = \sum_{a \in A} \pi(s,a) \sum_{s' \in S} P^a_{s,s'} \{ R^a_{s,s'} + \gamma V^\pi(s') \}

V^{\pi_s}(s) = \sum_{a \in A} \pi_s(s,a) \sum_{s' \in S} P^a_{s,s'} \{ R^a_{s,s'} + \gamma V^{\pi_s}(s') \}

by definition \pi(s,a) = \pi_s(s,a). Subtracting (3) and (4) we get:

\delta(s) = V^{\pi}(s) - V^{\pi_s}(s) = \gamma \sum_{a \in A} \pi(s,a) \sum_{s' \in S} P^a_{s,s'} \{V^\pi(s') - V^{\pi_s}(s')\}

We have V^\pi(s') - V^{\pi_s}(s') \ge V^\pi(s') - V^{\pi_{s'}}(s') = \delta(s'), which together with equation 5 gives us:

\delta(s)  \ge \gamma \sum_{a \in A} \pi(s,a) \sum_{s' \in S} P^a_{s,s'} \delta(s') = \gamma \sum_{s'\in S} \left(\sum_{a \in A} \pi(s,a) P^a_{s,s'} \right) \delta(s')

the summation on RHS represents convex combination of \delta and therefore we can use convex-combination(x) \ge x_{min} to give us:

\delta(s) \ge \gamma \,\, \inf \{\delta(s') \mid s' \in S\}

as inequality 7 holds for all s \in S, therefore this gives us:

\inf \{\delta(s') \mid s' \in S\} \ge \gamma \,\, \inf \{\delta(s') \mid s' \in S\}

as \gamma \in [0,1), therefore inequality 8 can only hold if \inf \{\delta(s') \mid s' \in S\} \ge 0 \Rightarrow \delta(s) \ge 0 \forall s \in S. Hence proved.

3.2 Bellman optimality condition

Note that our proof for existence of optimal policy is almost impractical. It requires us to find the optimal policy for every state, which can be highly inefficient if we have a large state space or action space. Here we will derive an equation that will allow us to later compute the optimal policy more efficiently.

We first denote the optimal policy by \pi^* and define V^*(s) = V^{\pi^*}(s) and Q^*(s) = Q^{\pi^*}(s).

Theorem* (Bellman Optimality Condition):  V^*(s) and Q^*(s) satisfy the following equations:

V^*(s) = \max_a Q^*(s,a)

V^*(s) = \max_a \sum_s' P^a_{s,s'} \{ R^a_{s,s'} + \gamma V^*(s') \}

Q^*(s,a) = \sum_s' P^a_{s,s'} \{ R^a_{s,s'} + \gamma \max_{a'} Q^*(s',a')\}

Proof: Let \pi^*(s,a) be the optimal policy. Then we have, V^*(s) = \sum_a \pi^*(s,a) Q^*(s,a) \le \max_a Q^*(s,a). To prove the first equation, all we have to show is that we can never have V^*(s) < \max_a Q^*(s,a). We will prove this via contradiction.

Let \exists s' \,\,s.t.\,\, V^*(s') < \max_a Q^*(s',a). We will use this fact to show that \pi^* is not optimal by deriving a better policy. We define this new policy as: \pi'(s) = \arg\max_a Q^*(s,a). Note that this new policy is a deterministic policy. Then from self-consistency equations we have:

V^*(s) = \sum_a' \pi^*(s,a) Q^*(s,a)

V^{\pi'}(s) = Q^{\pi'}(s, \pi'(s))

As before, we define \delta(s) =  V^{\pi'}(s) -V^*(s) =Q^{\pi'}(s, \pi'(s)) - \sum_a' \pi^*(s,a) Q^*(s,a). We then have:

\delta(s) =  V^{\pi'}(s) -V^*(s) =Q^{\pi'}(s, \pi'(s)) - \sum_a' \pi^*(s,a) Q^*(s,a)

Using the definition of \pi', as the policy that picks an action that maximizes Q^* values, we get:

\delta(s) \ge Q^{\pi'}(s, \pi'(s)) - Q^{*}(s, \pi'(s)).

Note that for the state s', the above inequality is strict. This follows from V^*(s') < \max_a Q^*(s,a') = Q^*(s, \pi(s')).

Let a = \pi'(s) then we have:

Q^{\pi'}(s, a) - Q^{*}(s, a) = \sum_{s'} P^a_{s,s'} (R^a_{s,s'} + \gamma V^{\pi'}(s')) - \sum_s' P^a_{s,s'} (R^a_{s,s'} + \gamma V^{*}(s'))

Q^{\pi'}(s, a) - Q^{*}(s, a) = \gamma \sum_{s'}P^a_{s,s'}  (V^{\pi'}(s') -V^{*}(s')) = \gamma \sum_{s'} P^a_{s,s'} \delta(s').

Giving us, \delta(s) \ge\gamma \sum_{s'} P^a_{s,s'} \delta(s').

using argument similar to earlier, we show that \inf_s \delta(s) \ge \gamma \inf_{s'} \delta(s') and as \gamma\in [0,1), we have \inf_s \delta(s) \ge  \Rightarrow \forall s, \delta(s) \ge 0.

But wait, this is not what we wanted to show. We want to show that \pi' > \pi^* or in other words, we want to show that \exists s \,\,s.t.\,\, \delta(s) > 0. But this follows easily since for the state s', the above inequality is strict. Hence, \pi^* cannot be the optimal policy, which is a contradiction.

The next 2 equations in the proof, easily follow from the first equation (try it yourself).

These three equations are called the Bellman optimality conditions.

Interesting observations from the above proof:

  1. For the optimal policy \pi^* satisfying V^{\pi^*}(s) = \max_a Q^{\pi^*}(s,a), we can still define a new policy \pi'(s)  = \arg\max_a Q(s,a). We can then follow the above proof to derive that the new policy is as good as the optimal policy, but not better. But this new policy \pi' is  deterministic. This shows that every MDP has a deterministic optimal policy. So you do not have to worry about being stochastic if you want to be optimal. This is a remarkably interesting fact about MDPs.
  2. If \pi does not satisfy the Bellman optimality condition, then we can follow the proof to derive a new policy \pi' given by \pi'(s) = \arg\max_a Q^{\pi}(s,a) such that \pi' > \pi. That is we can improve upon our current policy. This is one way of doing policy improvement. Given a policy, find its Q-values and then define a better policy as discussed. We can keep doing this, either forever or till we finally find a policy that satisfies the Bellman optimality condition.
  3. Lastly, notice how the reward terms simply cancel out. It beautifully shows that the proof does not depend upon how the reward function looks like.

 

3.3 Bellman backup operator

To solve an MDP means to find the optimal policy and the optimal value functions. Unfortunately, we cannot easily solve the Bellman optimality condition as they are non-linear due to max sitting in the equation. We now provide a strategy for computing V^*, Q^* by iteratively solving the optimality condition. Given V^* we can compute Q^* using the equations above. Therefore, in the remainder of this section we will be solely interested in computing V^*.

Let V: S \rightarrow R be the state-value function for an MDP then T: V \rightarrow V is the Bellman backup operator given by:

TV(s) = max_a \sum_{s'} P^a_{s,s'} \{R^a_{s,s'} + \gamma V(s') \}

We then prove that T satisfies the contraction property namely:

Theorem: \| TV_1 - TV_2 \|_{\infty} \le \gamma \| V_1 - V_2 \|_{\infty} where \| . \|_{\infty} is the max norm given by \| x \| = \max\{ |x_1|, \cdots |x_n| \}

Proof: \| TV_1 - TV_2 \|_{\infty} = \| \max_a \sum_{s'} P^a_{s,s'} \{R^a_{s,s'} + \gamma V_1(s') \} - \max_a \sum_{s'} P^a_{s,s'} \{R^a_{s,s'} + \gamma V_2(s') \} \|
we now use the fact that |\max_x f(x) - \max_x g(x)| \le \max_x |f(x) - g(x)|.

|\max_a \sum_{s'} P^a_{s,s'} \{R^a_{s,s'} + \gamma V_1(s') \} - \max_a \sum_{s'} P^a_{s,s'} \{R^a_{s,s'} + \gamma V_2(s') \}|
 \le \max_a | \sum_{s'} P^a_{s,s'} \{R^a_{s,s'} + \gamma V_1(s') \} -  \sum_{s'}P^a_{s,s'} \{R^a_{s,s'} + \gamma V_2(s') \}|
= \max_a \gamma |\sum_{s'}P^a_{s,s'} \{V_1(s') -V_2(s')\}|

since this holds for every s, we have

\| TV_1 - TV_2 \|_{\infty} \le \|\max_a \gamma |\sum_{s'}P^a_{s,s'} \{V_1(s') -V_2(s')\}|\|_{\infty}
\le \|\max_a \gamma \sum_{s'}P^a_{s,s'}|V_1(s') - V_2(s')|\|_{\infty}
\le \|\max_a \gamma \max_{s'}|V_1(s') -V_2(s')|\|_{\infty}
= \|\gamma \max_{s'}|V_1(s') -V_2(s')|\|_{\infty}
= \gamma \max_{s'}|V_1(s') -V_2(s')| = \gamma \|V_1 -V_2\|_{\infty}

Hence proved.

We now prove that T has a unique fixed point. A fixed point x of an operator satisfies Tx  = x. This also means T^n x = T^{n-1}( Tx) = T^{n-1}x \cdots = x.

Lemma: Bellman backup operator T has a unique fixed point.
Proof: Let V_1, V_2 be two fixed point of T. Whic means TV_1 = V_1 and TV_2 = V_2. Then using the contraction property of T we get:

0 \le \| TV_1 - TV_2\|_{\infty} \le \gamma \| V_1 - V_2\|_{\infty}
further, \| TV_1 - TV_2\|_{\infty} = \| V_1 - V_2\|_{\infty}.

Using these two equations and \gamma \in [0, 1) we get,

\| V_1 - V_2\|_{\infty} \le \gamma \| V_1 - V_2\|_{\infty} \Rightarrow \| V_1 - V_2\|_{\infty} \le 0. This gives us V_1 = V_2.

Corrollary: Optimal state-value function is a fixed point of its Bellman backup operator. By above lemma, it is also the fixed point.

This means that solving for the fixed point of this operator gives us our optimal value functions and optimal policy. We finally show that repeated application of Bellman backup operator to any state-value function V eventually converges to the optimal value function.

Theorem: \lim_{n\rightarrow \infty} T^nV = V^*  \forall V
Proof: Using the contraction property.
\| T^n V - V^* \|_{\infty} = \| T^n V - TV^*\|_{\infty} \le \gamma \| T^{n-1}V-V^*\|_{\infty}

Applying the contraction property n-1 times we get:
\| T^n V - V^*\|_{\infty} \le \gamma^n \| V - V^* \|_{\infty}

taking limits on both sides and using \gamma \in [0,1) we get,
\lim_{n \rightarrow \infty} \| T^n V - V^*\|_{\infty} \le \lim_{n\rightarrow} \gamma^n \| V - V^* \|_{\infty} = 0.

Here, we assume that value functions are bounded which follows from bounded reward values.

4 Conclusion

Applying backup operator to compute optimal policy is impractical in most cases as number of states become very large. In next blog post on this topic, we will discuss algorithms for efficient learning of optimal policies such as Q-learning, Monte Carlo methods etc. We will also look at interesting reinforcement learning problems such as deep reinforcement learning (reinforcement learning with state represented by deep neural network features).

5 References

  1. Reinforcement learning: An Introduction, R. Sutton and A. Bato.
  2. Scribes from Peter Abeel’s course for section 3