Questions tagged [deterministic-policy]

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Can two states have different actions in a deterministic policy? How to specify states which have probability linked with them in the policy?

The agent has two actions, a0 and a1, whose effects in each state σ0; . . . ; σ3 are described in Figure 1. The edges from actions are labeled with the probability that this transition occurs. For ...
Trileo Stark's user avatar
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Deep deterministic policy gradient : which network do I have to use for testing?

We know that Deep deterministic policy gradient (henceforth ddpg) is characterized by two kind of neural networks: one related to the critic $Q$ the other to the actor $\mu$ with parameters $\theta^\...
Siderius's user avatar
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Greedy policy definition

I've always seen as definition for the greedy policy the one that maximizes the action value function $q_{\pi} (s,a)$ over the actions $a$. How is this equivalent to the following one that I found on ...
Damuna's user avatar
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Discrete and continuous actions in the same environment

I am working on a RL environment that requires both discrete and continuous actions as input from the agent. I currently have a fine implementation of DDPG which I would like to use for the continuous ...
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policy gradient for non-differentiable policy

Is it possible to apply policy gradient if the parameters of policy are not differentiable? If not, is there any other algorithm for optimizing such type of policies? One example I'm thinking about ...
DiveIntoML's user avatar
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Policy evaluation in contextual bandit setting

I am currently reading a paper whose links is (Exploration Scavenging) http://delivery.acm.org/10.1145/1400000/1390223/p528-langford.pdf?ip=128.135.98.49&id=1390223&acc=ACTIVE%20SERVICE&...
Hunnam 's user avatar
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quick questions about a contextual bandit problem

I am currently reading the paper "Learning from Logged Implicit Exploration Data" https://arxiv.org/pdf/1003.0120.pdf. But I believe the questions I have can be answered without reading the whole ...
Hunnam 's user avatar
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Proof that any $\epsilon$-greedy policy is an improvement over any $\epsilon$-soft policy

In the book by Richard Sutton and Andrew Barto, "Reinforcement Learning - An Introduction", 2ed edition, at page 101 there is a proof, and I don't understand 1 passage of it. We want to ...
robertspierre's user avatar
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Neural Network and equally good predictions

There is a two-player game (discrete, deterministic, perfect information and so on) where - in some but not all states - a few moves may be equally good; i.e. they are symmetric and expert player will ...
tomash's user avatar
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Different algorithms categorized in reinforcement learning

For some time I am going through reinforcement learning, and have found a lot of diverse information specially in area of Policies (algorithms). I figured out that policies can be classified in On ...
Sandeep Bhutani's user avatar
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Q-learning shows worse results than value iteration

I'm trying to solve the same problem with different algorithms (Travel max possible distance with a car). While using value iteration and policy iteration I was able to get the best results possible ...
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Is a policy always deterministic in reinforcement learning?

In reinforcement learning, is a policy always deterministic, or is it a probability distribution over actions (from which we sample)? If the policy is deterministic, why is not the value function, ...
figs_and_nuts's user avatar