Questions tagged [temporal-difference]

For questions related to the temporal-difference reinforcement learning (RL) algorithms, which is a class of model-free (that is, they do not use the transition and reward function of the MDP) RL algorithms which learn by bootstrapping from the current estimate of the value function (that is, they use one estimate to update another estimate).

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1answer
43 views

Why not more TD(𝜆) in actor-critic algorithms?

Is there either an empirical or theoretical reason that actor-critic algorithms with eligibility traces have not been more fully explored? I was hoping to find a paper or implementation or both for ...
3
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1answer
62 views

How does Monte Carlo have high variance?

I was going through David Silver's lecture on reinforcement learning (lecture 4). At 51:22 he says that Monte Carlo (MC) methods have high variance and zero bias. I understand the zero bias part. It ...
1
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1answer
52 views

Understanding the loss function in deep Q-learning

I am trying to understand how deep Q learning (DQN) works. To my current understanding, each $Q(s, a)$ functions is estimated to be a function of a feature vector of its state $\phi$(s) and the weight ...
5
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1answer
60 views

What is the intuition behind TD($\lambda$)?

I'd like to better understand temporal-difference learning. In particular, I'm wondering if it is prudent to think about TD($\lambda$) as a type of "truncated" Monte Carlo learning?
3
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1answer
60 views

What is the intuition behind the TD(0) equation with average reward, and how is it derived?

In chapter 10 of Sutton and Barto's book (2nd edition) is given the equation for TD(0) error with average reward (equation 10.10): $$\delta_t = R_{t+1} - \bar{R} + \hat{v}(S_{t+1}, \mathbf{w}) - \hat{...
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0answers
14 views

N-tuple based tic tac toe diverges in temporal difference learning

I have n-tuple based tic tac toe. I already have perfect minimax player and perfectly trained table-based player. My n-tuple network consists of 8 different rows of 3 of the board as triplets having ...
0
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0answers
16 views

How is the general return-based off-policy equation derived?

I'm wondering how is the general return-based off-policy equation in Safe and efficient off-policy reinforcement learning derived $$\mathcal{R} Q(x, a):=Q(x, a)+\mathbb{E}_{\mu}\left[\sum_{t \geq 0} \...
4
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1answer
69 views

Convergence of semi-gradient TD(0) with non-linear function approximation

I am looking for a result that shows the convergence of semi-gradient TD(0) algorithm with non-linear function approximation for on-policy prediction. Specifically, the update equation is given by (...
3
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1answer
39 views

Confusion about temporal difference learning

I have a couple of small questions about the David Silver lecture about reinforcement learning, lecture slides (slides 23, 24). More specifically it is about the temporal difference algorithm: $$V(...
3
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1answer
117 views

How to show temporal difference methods converge to MLE?

In chapter 6 of Sutton and Barto (p. 128), they claim temporal difference converges to the maximum likelihood estimate (MLE). How can this be shown formally?
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1answer
75 views

Understanding TD(0) algorithm implementation

I came across the $TD(0)$ algorithm from Sutton and Barto: Clearly, the only difference of TD methods with the MC methods is that TD method is not waiting till the end of the episode to update the $...
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0answers
33 views

How do I know if the assumption of a static environment is made?

An important property of a reinforcement learning problem is whether the environment of the agent is static, which means that nothing changes if the agent remains inactive. Different learning methods ...
5
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1answer
296 views

Understanding the equation of TD(0) in the paper “Learning to predict by the methods of temporal differences”

In the paper Learning to predict by the methods of temporal differences (p. 15), the weights in the temporal difference learning are updated as given by the equation $$ \Delta w_t = \alpha \left(P_{t+...
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0answers
73 views

Infinite horizon in Reinforcement Learning

I read this article: "Towards Autonomous Data Ferry Route Design through Reinforcement Learning" by Daniel Henkel and Timothy X Brown. It specifies an infinite horizon problem where they use as a ...
5
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2answers
359 views

Why am I getting the incorrect value of lambda?

I am trying to solve for lambda using Temporal Difference Learning I am trying to figure out what lambda I need, to make TD(λ)=TD(1) but I get the incorrect value of lambda. Here is how I did: <...
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0answers
23 views

Why should we use TD prediction as opposed to TD control algorithms?

Consider a problem where we have a finite number of states and actions. Given a state and an action, we can easily know the reward and the next state (deterministic). The state space is really large ...
2
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1answer
73 views

What is the relation between Monte Carlo and model-free algorithms?

Monte Carlo (MC) methods are methods that use some form of randomness or sampling. For example, we can use an MC method to approximate the area of a circle inside a square: we generate random 2D ...
1
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1answer
196 views

How fast does Monte Carlo tree search converge?

How fast does Monte Carlo Tree Search converge? Is there proof that it does converge? How does it compare to Temporal Difference learning in terms of convergence speed (assuming the evaluation step ...
3
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1answer
108 views

Understanding the n-step off-policy SARSA update

In Sutton & Barto's book (2nd ed) page 149, there is the equation 7.11 I am having a hard time understanding this equation. I would have thought that we should be moving $Q$ towards $G$, where $...
1
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1answer
54 views

On-policy distribution for Emphatic TD

The book by Sutton and Barto discussed in section 11.8 that the convergence of off-policy TD function approximation can be improved by correcting for distribution of states encountered. The section ...
4
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1answer
87 views

What are temporal-difference and Monte Carlo methods intuitively?

Intuitively, how do temporal-difference and Monte Carlo methods work in reinforcement learning? How can they be used to solve the reinforcement learning problem?