Gradient Monte Carlo Prediction

Monte Carlo updating is delayed because the final return is needed.

Programming

The Delayed Update

Gradient Monte Carlo Prediction evaluates a policy by learning the weights of a differentiable value function. The policy π generates an episode. During that episode, the algorithm observes states and performs computations, but it does not change the weight vector θ. The weight change waits until the episode has ended, because the final return needed by the Monte Carlo update is not available earlier.

When does the return become available, and when can the algorithm change the weights?

The Episode-to-Update Path

The algorithm can be understood as a sequence of connected steps. First, policy π generates an episode. Next, the algorithm considers each time step from 0 through T − 1. For each visited state S_t, it uses the return G_t, the current estimated value v̂(S_t, θ), and the gradient ∇v̂(S_t, θ). The difference between G_t and v̂(S_t, θ) indicates the prediction error. The gradient indicates how the estimated value responds to changes in the weights, so it determines the weight-space direction in which the estimate can be adjusted.

What happens from generating an episode under the policy to changing the value-function weights?

Gradient Monte Carlo Prediction is a delayed Monte Carlo method that uses gradient descent to update the weights of a differentiable value function after an episode supplies the needed returns.

Reading the Weight Adjustment

For a time step t, compare the observed return G_t with the current estimate v̂(S_t, θ). Their difference is the prediction error: G_t − v̂(S_t, θ). If the estimate differs from the return, the weights need an adjustment. The gradient ∇v̂(S_t, θ) supplies the direction in weight space associated with changing the value estimate. The update therefore uses both pieces: the prediction error determines what correction is needed, and the gradient determines how that correction is applied to the weights.

How do the return difference and the value-function gradient determine the update?

Following One Visited State

A policy-generated episode contains a state S_t. The algorithm has a return G_t, a current estimate v̂(S_t, θ), and a gradient ∇v̂(S_t, θ). What information controls the weight adjustment for this state?

Compare the target and estimate: The algorithm compares G_t with v̂(S_t, θ). Their difference identifies the prediction error for the visited state.

Use the gradient: The gradient ∇v̂(S_t, θ) describes how the differentiable value estimate depends on the weights.

Adjust the weights: The prediction error and the gradient are combined to determine the correction to θ. This correction is applied after the return is available.

The return difference determines the correction signal, while the gradient determines how that signal changes the value-function weights.

Eligibility Trace Memory

A direct implementation could retain the feature vector from every time step and use those vectors when the episode ends. The eligibility-trace implementation avoids that collection of separate vectors. It maintains an additional vector memory called the eligibility trace. Whenever a feature vector is encountered, the trace incorporates a summary of the vector information seen so far. The trace is therefore a running summary rather than a list of every feature vector.

How can one eligibility trace summarize feature-vector contributions from many visited time steps?

Reconstructing the Overall Update

At the end of the episode, the final return is available and the eligibility trace contains the combined information from the encountered feature vectors. The algorithm can then combine the return with that trace to recreate the same overall update that would have resulted from handling the sequence of Monte Carlo contributions. The important result is equivalence of the overall update, not a series of during-episode changes to θ.

How does the end-of-episode eligibility trace represent the same combined contribution as the feature vectors from all time steps?

Why the Summary Is Enough

An episode visits several states and produces feature vectors at each time step. The implementation must make an update after the episode ends, when the return is known. Compare retaining every vector with maintaining an eligibility trace.

Direct route: Retain access to the feature vector from each time step until the episode ends.

Trace route: Add the information from each encountered feature vector into the eligibility trace as the episode unfolds.

End-of-episode combination: When the return becomes available, combine it with the trace. The trace represents the combined contribution needed for the overall update.

The eligibility trace provides a compact running summary that allows the end-of-episode implementation to reproduce the same overall update without separately retaining every feature vector.

LMS as a Special Name

The general linear Monte Carlo update and LMS are not unrelated methods. LMS is the name used for the same linear Monte Carlo update in the simpler case where the return is one reward received at the end of the episode and there is no discounting. Therefore, LMS is a special case under stated conditions, not a replacement for the broader idea of delayed Monte Carlo updating.

What is shared by the general linear Monte Carlo update and LMS, and when does the LMS name apply?

Name	What it describes	Conditions highlighted
Linear Monte Carlo update	The broader delayed linear Monte Carlo update	Uses episode returns for the value update
LMS	The same update under simpler conditions	One reward at the end of the episode and no discounting

Mistakes to Avoid

Assuming that computation during the episode means the weights change during the episode.
The trace can accumulate information while the weight vector remains unchanged. The method waits for the final return before applying the weight update.
Fix: Separate trace accumulation from the end-of-episode change to θ.
Calling every linear Monte Carlo update LMS.
The LMS name is reserved for the stated special case with one reward at the end of the episode and no discounting.
Fix: Use linear Monte Carlo update for the broader method and LMS for that special case.
Treating the gradient as the prediction error.
The prediction error compares the return with the estimate. The gradient describes how the estimate depends on the weights.
Fix: Keep the two roles distinct: the return difference supplies the correction signal, and the gradient supplies the weight-space direction.
Assuming a differentiable value function is optional.
Gradient Monte Carlo uses the value-function gradient with respect to the weights.
Fix: Use a differentiable value function so ∇v̂(S_t, θ) is available.

Practice the Trace

MEDIUM

A policy generates an episode with several time steps. During the episode, the implementation accumulates an eligibility trace but does not change θ. At the end, the return G_t becomes available. Explain what information is combined to produce the overall update, why the update is called offline, and under what conditions the update may be called LMS.

Hints

Separate what happens during the episode from what happens after the episode.
Mention the trace as a summary of feature-vector information.
LMS requires one reward at the end of the episode and no discounting.

Your attempt (not saved)

What do you think happens?

The algorithm has accumulated an eligibility trace, but the episode has not ended. Can it complete the Monte Carlo weight update?

Yes, because the trace alone is sufficient
No, because the final return is still needed
Yes, because the policy supplies the return immediately

Reveal answer

Answer: No, because the final return is still needed.

The trace summarizes feature-vector information, but the Monte Carlo update also requires the final return. The weight change waits until the episode has ended.

Summary

Gradient Monte Carlo evaluates policy π by learning the weights of a differentiable value function.
The algorithm waits until an episode ends because the required Monte Carlo return is then available.
During the episode, an eligibility trace can summarize the feature vectors encountered without separately retaining every vector.
At the end, the return and the trace recreate the same overall update that the sequence of Monte Carlo contributions would produce.
LMS is the name for the corresponding simpler case with one reward at the end of the episode and no discounting.

Key Takeaways

Gradient Monte Carlo changes θ after the episode, not during it.
The return difference G_t − v̂(S_t, θ) supplies the prediction correction, while ∇v̂(S_t, θ) determines how the weights are adjusted.
An eligibility trace stores a running summary of feature-vector contributions.
The end-of-episode trace and return reproduce the same overall update efficiently.
LMS names a special linear Monte Carlo case rather than a separate general principle.