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Questions tagged [stochastic-policy]

For questions related to the concept of a stochastic policy (as defined in reinforcement learning), which is a function from a state to a probability distribution over actions (from that state).

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Can a policy with gaussian distribution allow two distinct optimal actions to have distinctively high probabilities?

As an example to show the benefits of stochastic policy, I often have seen the below grid world example. Five blocks in a row. the first, third, and fifth are white(distinguishable states), and the ...
user3315463's user avatar
0 votes
1 answer

Consequence of Dvoretzky Stochastic Approximation Theorem

I am trying to understand all the steps to prove the TD0 algorithm, and I am following a proof which uses a theorem of Tommi Jaakkola, Michael I. Jordan and Satinder P. Singh, in the paper: On the ...
Kareit's user avatar
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4 votes
2 answers

How do we estimate the value of a stochastic policy?

I'm learning about reinforcement learning, particularly policy gradient methods and actor-critic methods. I've noticed that many algortihms use stochastic policies during training (i.e. they select ...
mac_or_cheese's user avatar
3 votes
1 answer

How is $v_*(s) = \max_{\pi} v_\pi(s)$ also applicable in the case of stochastic policies?

I am reading Sutton & Bartos's Book "Introduction to reinforcement learning". In this book, the defined the optimal value function as: $$v_*(s) = \max_{\pi} v_\pi(s),$$ for all $s \in \...
Tamar's user avatar
  • 33
2 votes
1 answer

Is a learned policy, for a deterministic problem, trained in a supervised process, a stochastic policy?

If I trained a neural network with 4 outputs (one for each action: move down, up, left, and right) to move an agent through a grid (deterministic problem). The output of the neural network is a ...
Xtalker's user avatar
  • 21
2 votes
1 answer

Did Alphago zero actually beat Alphago 100 games to 0?

tl;dr Did AlphaGo and AlphaGo play 100 repetitions of the same sequence of boards, or were there 100 different games? Background: Alphago was the first superhuman go player, but it had human tuning ...
EngrStudent's user avatar
3 votes
1 answer

In the policy gradient equation, is $\pi(a_{t} | s_{t}, \theta)$ a distribution or a function?

I am learning about policy gradient methods from the Deep RL Bootcamp by Peter Abbeel and I am a bit stumbled by the math presented. In the lecture, he derives the gradient logarithm likelihood of a ...
calveeen's user avatar
  • 1,271
3 votes
1 answer

What's the value of making the RL agent's output stochastic opposed to deterministic?

I have a question about a reinforcement learning problem. I'm training an agent to add or delete pixels in a [12 x 12] 2D space (going to be 3D in the future). Its action space consists of two ...
SumakuTension's user avatar
3 votes
1 answer

Is it possible for value-based methods to learn stochastic policies?

Is it possible for value-based methods to learn stochastic policies? I'm trying to get a clear picture of the different categories for RL algorithms, and while doing so I started to think about ...
Krrrl's user avatar
  • 211
8 votes
3 answers

What is the difference between a stochastic and a deterministic policy?

In reinforcement learning, there are the concepts of stochastic (or probabilistic) and deterministic policies. What is the difference between them?
nbro's user avatar
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16 votes
3 answers

Is the optimal policy always stochastic if the environment is also stochastic?

Is the optimal policy always stochastic (that is, a map from states to a probability distribution over actions) if the environment is also stochastic? Intuitively, if the environment is ...
nbro's user avatar
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3 votes
1 answer

Can Q-learning be used to derive a stochastic policy?

In my understanding, Q-learning gives you a deterministic policy. However, can we use some technique to build a meaningful stochastic policy from the learned Q values? I think that simply using a ...
Hammer. Wang's user avatar