Questions tagged [monte-carlo-methods]

For questions related to the Monte Carlo methods in reinforcement learning and other AI sub-fields. ("Monte Carlo" refers to random sampling of the search space.)

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187 views

How to apply hyperparameter optimization on Monte Carlo Tree Search?

I have a basic MCTS agent for the game of Hex (a turn based game). I want to tune the parameters of UCT (the Cp parameter) and the number of rollouts parameter. Where do I have to begin? The problem ...
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1answer
145 views

Monte Carlo epsilon-greedy Policy Iteration: monotonic improvement for all cases or for the expected value?

I was going through university slides and this particular slide is trying to prove that in a Monte Carlo Policy Iteration algorithm using an epsilon-greedy policy, the state Values (V-Values) are ...
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1answer
53 views

Understanding the W term in off policy monte carlo learning

In Sutton and Barto's RL textbook they included the following pseudocode for off policy Monte Carlo learning. I am a little confused, however, because to me it looks like the W term will become ...
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416 views

Difficulty understanding Monte Carlo policy evaluation (state-value) for gridworld

I've been trying to read Sutton & Barto book chapter 5.1, but I'm still a bit confused about the procedure of using Monte Carlo policy evaluation (p.92), and now I just cant proceed anymore coding ...
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40 views

In reinforcement learning what do we mean by a model? [duplicate]

I was going through the chapter "Monte Carlo Methods" from the book "Reinforcement Learning" by Sutton and Barto. The author says that when a model is not available then it is useful to estimate ...
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1answer
121 views

What does the figure "Blackjack Value Function..." from Sutton represent?

I came across this graph in David Silver's youtube lecture and Sutton's book on reinforcement learning. Can anyone help me understand the graph? From the graph, for 10000 episodes what i see is ...
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1answer
252 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 ...
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1answer
696 views

MCTS: How to choose the final action from the root

When the time allotted to Monte Carlo tree search runs out, what action should be chosen from the root? The original UCT paper (2006) says bestAction in their ...
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1answer
128 views

Why is an average of all returns used to update the value in the first-visit MC control?

In Sutton & Barto's Reinforcement Learning: An Introduction, in page 83 (101 of the pdf), there is a description of first-visit MC control. In the phase where they update $Q(s, a)$, they do an ...
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0answers
41 views

Why is this Monte Carlo approach scalable for a growing number of states variables and action variables?

I am reading a research paper on the formulation of MDP problems to ICU treatment decision making: Treatment Recommendation in Critical Care: A Scalable and Interpretable Approach in Partially ...
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1answer
139 views

Why Monte Carlo epsilon-soft approach cannot compute $\max Q(s,a)$?

I am new to Reinforcement learning and am currently reading up on the estimation of Q $\pi(s, a)$ values using MC epsilon-soft approach and chanced upon this algorithm. The link to the algorithm is ...
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1answer
33 views

Can I use my previous estimate of the state-action values as initialisation in GLIE-Monte Carlo Control?

I am trying to implement a tabular-based GLIE Monte-Carlo learning algorithm. So I repeat n times: create observations using my previous policy $\pi_{n-1}(s)$ update my state-action values using ...
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43 views

How to show Monte Carlo methods converge to an estimate which minimizes mean squared error?

In chapter six of Sutton and Barto (p.128), they claim Monte Carlo methods converge to an estimate minimizing the mean squared error. How can this be shown formally? Bump
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2answers
117 views

How to stop evaluation phase in reinforcement learning with epsilon-greedy Monte Carlo agent?

I have implemented an epsilon-greedy Monte Carlo reinforcement learning agent like suggested in Sutton and Barto's RL book (page 101). As far as I understood epsilon-greedy agents so far, the ...
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1answer
297 views

How is Monte Carlo different from model-based methods?

I was going through an article where it is mentioned: The Monte-Carlo methods require only knowledge base (history/past experiences)—sample sequences of (states, actions and rewards) from the ...
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1answer
208 views

Why does GLIE+MC Control Algorithm use a single episode of Monte Carlo evaluation?

GLIE+MC control Algorithm: My question is why does this algorithm use only a single Monte Carlo episode (during PE step) to compute the $Q(s,a)$? In my understanding this has the following drawbacks: ...
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80 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 ...
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1answer
112 views

Why is Monte Carlo used as the tree search algorithm for AlphaGo?

Could a better algorithm other than Monte Carlo be used for the AlphaGo computer? Why didn't the DeepMind team think of choosing another kind of algorithm rather than spending time on their neural ...
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1answer
321 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 ...
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18 views

How is GARB implemented in PGRD-DL to calculate gradients w.r.t. internal rewards?

In section 3 of this paper the author outlines how GARB was adapted to reduce the variance in updating parameters to an internal reward function estimator. I have read it a number of times and ...
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1answer
80 views

Similarities and differences between UCT algorithms in (i), (ii), (iii) and (iv)?

I am trying to understand the similarities and differences between: (i) the UCT algorithm in Kocsis and Szepesvári (2006); (ii) the UCT algorithm in Section 3.3 of Browne et al (2012); (iii) the MCTS ...
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69 views

Why didn't champion of the Go game manage to win the last game against AlphaGo, after winning the 4th one?

In the documentary about the match, it is said that after losing the 4th game, AlphaGo came back stronger and started to play in a weird way (not human-like) and it was pretty impossible to be beaten. ...
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1answer
131 views

Do we need the transition probability function when calculating the importance sampling ratio?

I am reading the book titled "Reinforcement Learning: An Introduction" (by Sutton and Barto). I am at chapter 5, which is about Monte Carlo methods, but now I am quite confused. There is one thing I ...

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