Suggest edit — Sarsa (Lambda)

Title

Name

Note

---
title: "Sarsa (Lambda)"
source: https://www.jemoka.com/posts/kbhsarsa_lambda/
---

Sarsa (Lambda) is SARSA with Eligibility Traces (\(\lambda\)).
Previous approaches to deal with Partially Observable Markov Decision Process:
memory-based state estimation (beliefs) special planning methods Key question: Can we use MDP reinforcement learning to deal with POMDPs?
Background Recall MDP SARSA:
\begin{equation} Q(s,a) \leftarrow Q(s,a) + \alpha \qty [(r + \gamma Q(s&rsquo;, a&rsquo;)) - Q(s,a)] \end{equation}
Recall that, sparse rewards with SARSA can take a long time to learn because it takes time to backpropgate.
Hence, we use Eligibility Traces, which keeps track of what&rsquo;s &ldquo;eligible&rdquo; for updates:
let \(\lambda\) be some decay parameter, we have:
\begin{equation} \delta = r + \gamma Q(s&rsquo;,a&rsquo;) - Q(s,a) \end{equation}
and, we can write:
\begin{equation} Q(s,a) \leftarrow Q(s,a) + \lambda \delta N(s,a) \end{equation}
where by the visit counts are discounted such that:
\begin{equation} N(s,a) \leftarrow \gamma \lambda N(s,a) \end{equation}
Worry Inability of fully observing the state seems to invalidate the point of \(Q\) learning, SARSA, etc.
Applying Eligibility Traces to POMDPs Instead of \(N(s,a)\) a visitation count, we initialize some \(\eta(x,a)\) for observation + action and work on the rest of it in the same way.
At each step, we sample some reward \(r_{t+1}\) and observation \(x_{t+1}\) (and remember our current \(r_{t}, x_{t}\)). Then in which case:
\begin{equation} \eta(x_{t},a_{t}) = 1\ \text{(reset decay)} \end{equation}
and
\begin{equation} \forall (x\neq x_{t}, a \neq a_{t}): \eta(x,a) = \gamma \lambda \eta_{t-1}(x,a)\ \text{(decay others)} \\ \end{equation}
Using the new \(\eta\) values, we update \(Q(x, a)\) in the usualish manner:
\begin{equation} \delta_{t} := r_{t} + \gamma Q (x_{t+1}, a_{t+1}) - Q(x_{t}, a_{t}) \end{equation}
\begin{equation} \forall (x,a): Q(x,a) = Q(x,a)+a \delta_{t} \eta(x,a) \end{equation}
all values of \(\eta\) are established to \(0\) per episode.
Notably, we formulate \(x\) as a tuple of TWO observations in the past&mdash;meaning we have a single step of memory in the past and optimise over those.
This requires no belief propagation!! And note how the &ldquo;eligibility&rdquo; of each observation decays over time, such that the influence of that observation decays until we see the corresponding observation again.
aliasing an important failure of this is aliasing&ndash;where you maybe in one of two different places that has similar properties observationally, but taking actions at those states results in very different places.