# Questions tagged [sutton-barto]

For questions related to the book "Reinforcement Learning: An Introduction" (by Andrew Barto and Richard S. Sutton).

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### $\gamma^t$ in REINFORCE update (Sutton-Barto RL book Exercise 13.2)

I've struggled with solving exercise 13.2 from Reinforcement Learning: An Introduction Second Edition : Generalize the box on page 199, the policy gradient theorem (13.5), the proof of the policy ...
54 views

### Could you explain these 2 steps of the derivation of the Bellman equation as a recursive equation in Sutton & Barto?

I am reading the Sutton & Barto (2018) RL textbook. On page 59, it derives the recursive property of the state-value function as below. Could you explain the steps of third and fourth equality? ...
58 views

### Understanding SARSA with binary features and function approximation

I'm reading the Sutton and Barto Book on Reinforcement Learning (for oral exam preparation). However, I'm stuck on the algorithm for SARSA($\lambda$) with binary features and linear function ...
1 vote
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### Where is the problem: in batch TD(0) algorithm or in the code to solve AB problem in Sutton-Barto RL book?

Here is the batch TD(0) algorithm: Here is the AB example I want to solve using batch TD(0): And finally here is my Matlab code: % eps1: A 0 B 0 % eps2: B 1 % eps3: B 1 % eps4: B 1 % eps5: B 1 % ...
1 vote
172 views

### How does off-policy Monte Carlo weighted importance sampling bias converge to zero (Sutton & Barto Section 5.5)

On Section 5.5 (page 105) of Sutton & Barto's "Reinforcement Learning: An Introduction", they discuss the off-policy Monte Carlo method for learning the value function of a target policy ...
154 views

### Why is $\sum_{s} \eta(s)$ a constant of proportionality in the proof of the policy gradient theorem?

In Sutton and Barto's book (http://incompleteideas.net/book/bookdraft2017nov5.pdf), a proof of the policy gradient theorem is provided on pg. 269 for an episodic case and a start state policy ...
272 views

### Sutton & Barto: what are parametrized functions?

From "Reinforcement Learning: An introduction (2nd ed.)" by Richard S. Sutton and Andrew G. Barto, on page 59 Instead, the agent would have to maintain $v_\pi$ and $q_\pi$ as parameterized ...
1 vote
55 views

### Why will every action be sampled an infinite number of times?

I am reading the book Reinforcement Learning: An Introduction. Second edition (Richard S. Sutton and Andrew G. Barto). In the k-armed bandit problem using $\varepsilon$-greedy selection method, the ...
1 vote
187 views

1 vote
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### Unclear step in off-policy (every-visit) MC Control: why do we need the line: $A_t \neq \pi(S_t)$ then exit inner loop?

Could please some expert on reinforcement learning explain the red-box part in the following off-policy MC control? I mean I did not understand what (and why) is done in the step shown as a red-box. I ...
366 views

### Why does OpenAI's PPO algorithm not follow the discounting method used in Sutton & Barto?

As discussed in this question, the policy gradient algorithms given in Reinforcement Learning: An Introduction use the gradient \begin{align*} \gamma^t \hat A_t \nabla_{\theta} \log \pi(a_t \, | \, ...
105 views

### In Policy Gradient methods, why are actions always chosen from a Gaussian in the literature?

In Sutton's 2020 Reinforcement Learning text (in chapter 13.7 Policy Parameterization for Continuous Actions) it's stated actions [may be] chosen from a normal (Gaussian) distribution. However, I ...