# Questions tagged [policies]

For questions related to policies (as defined in reinforcement learning or other AI sub-fields).

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### 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-...
1 vote
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### What is the difference between a policy and rewards?

I don't understand the difference between a policy and rewards. Sure, a policy tells us what to do, but isn't the output of a neural network trained on rewards basically a policy (i.e. choose the ...
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### Is the initialisation of $V(s)$ and $\pi(s)$ really arbitrary in policy iteration?

In Sutton and Barto's book (Reinforcement learning: An introduction. MIT press, 2018), the algorithm "Policy Iteration" is: Here, $V(s)$ is initialized arbitrarily, meaning that I can ...
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### Why is there an inconsistency between my calculations of Policy Iteration and this Sutton & Barto's diagram?

In equation 4.9 of Sutton and Barto's book on page 79, we have (for the policy iteration algorithm): $$\pi'(s) = arg \max_{a}\sum_{s',r}p(s',r|s,a)[r+\gamma v_{\pi}(s')]$$ where $\pi$ is the previous ...
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### What's the benefit of repeating an action for a consecutive number of time steps?

What's the benefit of repeating an action for a consecutive number of time steps? Is there a way to tell if an agent in a given environment might perform better from repeated actions? I came across an ...
1 vote
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### Do we need multiple parallel environments to train in batches an on-policy algorithm?

When using an on-policy method in reinforcement learning, like advantage actor-critic, you shouldn't use old data from an experience buffer, since a new policy requires new data. Does this mean that ...
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### What are some other real-life examples of simple policies but complex value functions?

Hado van Hasselt, a researcher at DeepMind, mentioned in one of his videos (from 7:20 to 8:20) on Youtube (about policy gradient methods) that there are cases when the policy is very simple compared ...
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### DQN learns to always choose the same action for all states

I have created an RL model that uses QBased policy with a neural network for estimating Q values. My action space is of 27 actions, where each action is a 3 tuple where each value can be 1, 2 or 3. ...
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### Are optimal policies always deterministic, or can there also be optimal policies that are stochastic?

Let $M$ be an MDP with two states, $A$ and $B$, where $A$ is the starting state, and you always transit to the final state $B$ using two possible actions. $A_1$ gives you rewards that are normally ...
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### Why do value iteration and policy iteration obtain similar policies even though they have different value functions?

I am trying to implement value and policy iteration algorithms. My value function from policy iteration looks vastly different from the values from value iteration, but the policy obtained from both ...
224 views

### How can we find the value function by solving a system of linear equations without knowing the policy?

An MDP is a Markov Reward Process with decisions, it’s an environment in which all states are Markov. This is what we want to solve. An MDP is a tuple $(S, A, P, R, \gamma)$, where $S$ is our state ...
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### Is my understanding of the value function, Q function, policy, reward and return correct?

I'm a beginner in the RL field, and I would like to check that my understanding of certain RL concepts. Value function: How good it is to be in a state S following policy π. ...
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### In reinforcement learning, why are policies defined as functions of states and not observations?

I am new to RL and I am following Sutton & Barto's book. My doubt is, when we talk about the policy of our agent, we say it is the probability of taking some action $a$ given the state $s$. ...
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The Sutton and Barto reinforcement learning textbook states that the value of a state under an optimal policy must equal the expected return for the best action from that state. That is, $$v_*(s) = \... 1 vote 0 answers 33 views ### How to allow RL systems to find better policies after code changes? Suppose that in version 1 of a reinforcement-learning system an optimal policy A got generated for executing a task. But, in a newer version 2 of that application (with new code changes), there ... 5 votes 0 answers 68 views ### Proof that there always exists a dominating policy in an MDP I think that it is common knowledge that for any infinite horizon discounted MDP (S, A, P, r, \gamma), there always exists a dominating policy \pi, i.e. a policy \pi such that for all policies \... 8 votes 3 answers 9k 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? 1 vote 1 answer 61 views ### Is the policy gradient expression in Fundamentals of Deep Learning wrong? I don't understand the policy gradient as explained in Chapter-9 (Deep Reinforcement Learning) of the book Fundamentals of deep learning. Here is the whole paragraph: Policy Learning via Policy ... 2 votes 1 answer 61 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 ... 1 vote 2 answers 229 views ### Why is the optimal policy for an infinite horizon MDP deterministic? Could someone please help me gain some intuition as to why the optimal policy for a Markov Decision Process in the infinite horizon case (agent acts forever) is deterministic? 7 votes 1 answer 5k views ### What is the difference between a stationary and a non-stationary policy? In reinforcement learning, there are deterministic and non-deterministic (or stochastic) policies, but there are also stationary and non-stationary policies. What is the difference between a ... 3 votes 2 answers 145 views ### How does the AlphaGo Zero policy decide what move to execute? I was going through the AlphaGo Zero paper and I was trying to understand everything, but I just can't figure out this one formula:$$ \pi(a \mid s_0) = \frac{N(s_0, a)^{\frac{1}{\tau}}}{\sum_b N(s_0,...
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I have implemented DQN algorithm and wonder why during testing, the best performance is achieved by a policy from about 300 episode, when mean Q values converge at about 800 episode? Mean Q-values ...
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### What is a "learned policy" in Q-learning?

I am completing an assignment at the moment. One of the assignment questions asks how you identified the learned policy and how you obtained it. The question is a reinforcement learning question, and ...
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### What does "stationary" mean in the context of reinforcement learning?

I think I've seen the expressions "stationary data", "stationary dynamics" and "stationary policy", among others, in the context of reinforcement learning. What does it mean? I think stationary policy ...
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### Why does having a fixed policy change a Markov Decision Process to a Markov Reward Process?

If a policy is fixed, it is said that a Markov Decision Process (MDP) becomes a Markov Reward Process (MRP). Why is this so? Aren't the transitions and rewards still parameterized by the action and ...
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1 vote
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### What kind of reinforcement learning method does AlphaGo Deepmind use to beat the best human Go player?

In reinforcement learning, there are model-based versus model-free methods. Within model-based ones, there are policy-based and value-based methods. AlphaGo Deepmind RL model has beaten the best Go ...
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1 vote
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### Why does the value of state change depending on the policy used to get to that state?

From what I understand, the value function estimates how 'good' it is for an agent to be in a state, and a policy is a mapping of actions to state. If I have understood these concepts correctly, why ...
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### Off-policy full-random training in easy-to-explore environment

Let say we are in an environment where a random agent can easily explore all the states of an environment (for example: tic-tac-toe). In those environments, using off-policy algorithm, is it a good ...
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### Why do we have two similar action selection strategies for UCB1?

In the literature, there are at least two action selection strategies associated with the UCB1's action selection strategy/policy. For example, in the paper Algorithms for the multi-armed bandit ...
1 vote
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I am training a network through reinforcement learning. The policy network learns rotations, but depending on the actual input (state), the output of the network should be restricted to be in certain ...
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### Why doesn't value iteration use $\pi(a \mid s)$ while policy evaluation does?

I was looking at the Bellman equation, and I noticed a difference between the equations used in policy evaluation and value iteration. In policy evaluation, there was the presence of $\pi(a \mid s)$, ...
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### 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?
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### Is a policy in reinforcement learning analogous to a field such as APF?

If a policy maps states to actions in reinforcement learning, then for a path planning with obstacles, can't we simply use Artificial Potential Field fields for path planning and model policy ...
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### Is it common to have extreme policy's probabilities?

I have implemented several policy gradient algorithms (REINFORCE, A2C, and PPO) and am finding that the resultant policy's action probability distributions can be rather extreme. As a note, I have ...
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### What is meant by "generate the data" in describing the difference between on-policy and off-policy?

From the book: Sutton, Richard S.,Barto, Andrew G.. Reinforcement Learning (Adaptive Computation and Machine Learning series) (p. 100). The MIT Press. Kindle Edition. " following is stated: "...
1 vote
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### Does the off-policy evaluation work for non-stationary policies?

As the title says, in reinforcement learning, does the off-policy evaluation work for non-stationary policies? For example, IS (importance sampling)-based estimators, such as weighted IS or doubly ...
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### Why is the policy not a part of the MDP definition?

I'm reading an article on reinforcement learning, and I don't understand why the agent's policy $\pi$ is not part of definition of Markov Decision process(MDP): Bu, Lucian, Robert Babu, and Bart De ...
I have a dataset which includes states, actions, and reward. The dataset includes information on the transition, i.e., $p(r,s' \mid s,a)$. Is there a way to estimate a behavior policy from this ...