23
votes
Accepted
How to define states in reinforcement learning?
The problem of state representation in Reinforcement Learning (RL) is similar to problems of feature representation, feature selection and feature engineering in supervised or unsupervised learning.
...
- 30.4k
17
votes
How to stay a up-to-date researcher in ML/RL community?
There are some wonderful resources for keeping up to date in the ML community. Here are just a handful that a coworker showed me:
Deep Learning Monitor: this site contains hot and new papers along ...
- 2,359
13
votes
Accepted
Is there a fundamental difference between an environment being stochastic and being partially observable?
I think the distinction is made more for conceptual reasons, which has practical implications, so let me review the usual definitions of a stochastic and partially observable environment.
A stochastic ...
- 39.7k
12
votes
How to define states in reinforcement learning?
A common early approach to modeling complex problems was discretization. At a basic level, this is splitting a complex and continuous space into a grid. Then you can use any of the classic RL ...
- 9,207
8
votes
Is there a fundamental difference between an environment being stochastic and being partially observable?
A few points I'd like to add (without repeating the info already provided by nbro's answer):
I think you're half-right, in that indeed we can probably always model randomness as hidden information (e....
- 10.1k
7
votes
Accepted
Can Q-learning be used in a POMDP?
The usual (as presented in Reinforcement Learning: An Introduction) $Q$-learning and SARSA algorithms use (and update) a function of a state $s$ and action $a$, $Q(s, a)$. These algorithms assume that ...
- 39.7k
7
votes
How are the reward functions $R(s)$, $R(s, a)$ and $R(s, a, s')$ equivalent?
In general the different reward functions $R(s)$, $R(s, a)$ and $R(s, a, s')$ are not equivalent mathematically, so you will not find any formal proof.
It is possible for the functions to resolve to ...
- 30.4k
7
votes
Why is the policy not a part of the MDP definition?
The MDP defines the environment (which corresponds to the task that you need to solve), so it defines e.g. the states of the environment, the actions that you can take in those states, the ...
- 39.7k
7
votes
Accepted
What is ergodicity in a Markov Decision Process (MDP)?
In short, the relevant class of a MDPs that guarantees the existence of a unique stationary state distribution for every deterministic stationary policy are unichain MDPs (Puterman 1994, Sect. 8.3). ...
- 580
6
votes
Accepted
What techniques are used to make MDP discrete state space manageable?
tl:dr
Read chapter 9 of an Introduction of Reinforcement Learning
There is definitely a problem (a curse if you will) when the dimensionality of a task (MDP) grows. For fun, lets extend your problem ...
- 3,787
6
votes
Accepted
What are some resources on continuous state and action spaces MDPs for reinforcement learning?
There is a small survey of continuous states, actions and time in reinforcement learning in my thesis proposal.
Regarding books, Reinforcement Learning: State-of-the-Art seems to be pretty up-to-date ...
- 2,099
6
votes
Accepted
Why does the definition of the reward function $r(s, a, s')$ involve the term $p(s' \mid s, a)$?
Expectation of reward after taking action $a$ in state $s$ and ending up in state $s'$ would simply be
\begin{equation}
r(s, a, s') = \sum_{r \in R} r \cdot p(r|s, a, s')
\end{equation}
The problem ...
- 2,336
5
votes
What is a time-step in a Markov Decision Process?
In a Markov Decision Process (MDP) model, we define a set of states ($S$), a set of actions ($A$), the rewards ($R$), and the transition probabilities $P(s' \mid s, a)$. The goal is to figure out the ...
- 365
5
votes
How can you represent the state and action spaces for a card game in the case of a variable number of cards and actions?
Instead of having the AI learn what action to take, you can alternatively train it to judge how "good" a position is. In order to determine what move to make, you don't ask the AI "This is the current ...
- 151
5
votes
How are the reward functions $R(s)$, $R(s, a)$ and $R(s, a, s')$ equivalent?
Let $R(s)$ denote a probability distribution over rewards that our agent may get in some MDP as a reward for entering a state $s$. The easiest case is to demonstrate that we can also choose to write ...
- 10.1k
5
votes
Accepted
Benchmarks for reinforcement learning in discrete MDPs
Although I am not aware of any "benchmark problems" for (discrete) MDPs, I'll comment a bit on possible benchmarks and I will show some benchmarks used to test POMDP algorithms.
MDP vs POMDP
In ...
- 365
5
votes
Accepted
Why can we take the action $a$ from the next state $s'$ in the max part of the Q-learning update rule, if that action doesn't lead to any reward?
I'm using OpenAI's cartpole environment. First of all, is this environment not Markov?
The OpenAI Gym CartPole environment is Markov. Whether or not you know the transition probabilities does not ...
- 30.4k
5
votes
Accepted
Does stochasticity of an environment necessarily mean non-stationarity in MDPs?
Is a stochastic environment necessarily also non-stationary?
No.
A stochastic environment (i.e. an MDP with a transition model $p(s', r \mid s, a)$) can be stationary (i.e. $p$ does not change over ...
- 39.7k
5
votes
Accepted
Are optimal policies always deterministic, or can there also be optimal policies that are stochastic?
I think the result you are referring to is the one that says that there always exists a deterministic optimal policy for an MDP. This is true. But note that this does not imply that a stochastic ...
- 580
4
votes
Accepted
What is the relation between a policy which is the solution to a MDP and a policy like $\epsilon$-greedy?
for example, the "greedy policy" always chooses the action with the highest expected return, no matter which state we are in
The "no matter which state we are in" there is generally not true; in ...
- 10.1k
4
votes
Can the rewards be stochastic when the transition model is deterministic?
My question is, would $r_1 =r_2$?
That's usually up to you as the designer of the system.
Usually when you declare that you have "a deterministic environment", you imply that both $s'$ and $r$ are ...
- 30.4k
4
votes
What is a time-step in a Markov Decision Process?
In the reinforcement learning setting, an agent interacts with an environment in (discrete) time steps, which are incremented after the agent takes an action, receives a reward and the "system" (the ...
- 39.7k
4
votes
How can you represent the state and action spaces for a card game in the case of a variable number of cards and actions?
Filling values is totally fine. In the case of image recognition the filling will be the background of the image (examples). For example in Belot you have total of 32 cards, which can be 32 boolean ...
- 276
4
votes
Accepted
Why am I getting the incorrect value of lambda?
$TD(\lambda)$ return has the following form:
\begin{equation}
G_t^\lambda = (1 - \lambda) \sum_{n=1}^{\infty} \lambda^{n-1} G_{t:t+n}
\end{equation}
For you MDP $TD(1)$ looks like this:
\begin{align}
...
- 2,336
4
votes
Accepted
Reinforcement learning with action consisting of two discrete values
You would still be picking a single action. Your action space is now $\mathcal{A} = \mathcal{O} \times \mathcal{I}$ where I've chosen $\mathcal{O}$ to be the set of possible orders from your problem ...
- 4,685
4
votes
Can you convert a MDP problem to a Contextual Multi-Arm Bandits problem?
The main difference between an MDP and contextual bandit setting is time steps and state progression. If those are important to the problem you want to solve, then it is not possible to convert.
...
- 30.4k
4
votes
Accepted
Is the state transition matrix known to the agents in a Markov decision processes?
In reinforcement learning (RL), there are some agents that need to know the state transition probabilities, and other agents that do not need to know. In addition, some agents may need to be able to ...
- 30.4k
4
votes
Accepted
Reward interpolation between MDPs. Will an optimal policy on both ends stay optimal inside the interval?
I believe the claim is true. Here is my attempt at a proof.
Let us consider the optimal infinite horizon value function $V_\alpha^*$ of $\mathcal{M}_\alpha$ at an arbitrary state $s \in S$.
The value $...
- 580
4
votes
Accepted
In reinforcement learning, why are policies defined as functions of states and not observations?
Ultimately, a policy must be such that is is possible for an agent to execute it.
If the policy depends on the state, the implicit assumption is that the agent has knowledge of the state and can ...
- 580
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