# Questions tagged [deterministic-policy]

For questions related to the concept of a "deterministic policy" (as defined in reinforcement learning).

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### What is an example of an *optimal* stochastic policy that assigns a nonzero probability to an action with a lower expected value?

A stochastic policy means that an agent has probabilities of choosing their available actions, given a state: $\pi(a|s)$. However in an optimal stochastic policy for a given state, you would assume ...
707 views

### Is Q-learning only capable of learning a deterministic policy?

I was following a reinforcement learning course on coursera and in this video at 2:57 the instructor says Expected SARSA and SARSA both allow us to learn an optimal $\epsilon$-soft policy, but, Q-...
93 views

### How is policy iteration capable of improving on a deterministic policy?

Given a policy $\pi$ and the improved version upon it using policy iteration $\pi'$ we have, for $\forall s \in S$, $v_{\pi'}(s)\geq v_{\pi}(s)$. I think the way we choose $\pi'$ makes it ...
273 views

### Determining to terminate at a reward or not

I am practicing the Bellman equation on Grid world examples and in this scenario, there are numbered grid squares where the agent can choose to terminate and collect the reward equal to the amount ...
103 views

### 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 ...
485 views

### 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 ...
2k views

### What is the loss for policy gradients with continuous actions?

I know with policy gradients used in an environment with a discrete action space are updated with $$\Delta \theta_{t}=\alpha \nabla_{\theta} \log \pi_{\theta}\left(a_{t} \mid s_{t}\right) v_{t}$$ ...
27 views

### Do we assume the policy to be deterministic when proving the optimality?

In reinforcement learning, when we talk about the principle of optimality, do we assume the policy to be deterministic?
15k views

### 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?
425 views

### What is the motivation behind using a deterministic policy?

What is the motivation behind using a deterministic policy? Given that the environment is uncertain, it seems stochastic policy makes more sense.