Questions tagged [state-spaces]

For questions about state spaces, in the context of reinforcement learning or other AI sub-fields.

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Defining states and possible actions in Q learning

I am trying to define the number of states and possible actions for a reinforcement learning problem that I want to solve with Q-learning, but I am a bit confused, as I'm totally new to reinforcement ...
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1answer
57 views

What is the difference between terminal state, nonterminal states and normal states?

In Sutton & Barto's Reinforcement Learning: An Introduction, page 54, the authors define the terminal state as following: Each episode ends in a special state called the terminal state But the ...
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3answers
103 views

What exactly are partially observable environments?

I have trouble understanding the meaning of partially observable environments. Here's my doubt. According to what I understand, the state of the environment is what precisely determines the next state ...
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25 views

Task/Process Scheduling Using DQN: Agent Does Not Learn

I have a problem statement where a couple of smartphones, for example inside a shopping mall, can migrate their time-consuming tasks/processes like image processing to an edge server located nearby. ...
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Is there any thumb rule on the cardinality of state space in order to use the parameterized function to estimate value functions?

Value functions for a given MDP can be learned in at least two ways by experience. The first way (tabular calculation) is generally used in the case of state spaces that are small enough. The second ...
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9 views

Encode states based on after-the-fact Similarity?

The question is what can you do in case where encoding STATE SIMILARITY is dependent on the interaction i.e. it is known after the fact. For example in chess you may have a State’s (S1 and S2) where a ...
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8 views

Is it possible to use agent coordinates directly as part of the state

I am working on graph optimisation problem using DQN - the graph is represented as an adjacency matrix and an agent moves through this matrix removing edges between nodes (add a 0) or adding edges ...
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1answer
59 views

What is the difference between environment states and agent states in terms of Markov property?

I'm going through the David Silver RL course on YouTube. He talks about environment internal state $S^e_t$, and agent internal state $S^a_t$. We know that state $s$ is Markov if $\mathbb{P}\{S_t=s|S_{...
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1answer
56 views

Q-learning in gridworld with random board

I'm trying to use Q-learning in order to solve Wumpus world environment. Wumpus world is a toy problem on 4x4 gridworld. The agent starts in entry position of the cave, looks for gold (agent can sense ...
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1answer
47 views

How can I compress the states of a reinforcement learning agent?

I'm working on a problem that involves an RL agent with very large states. These states consist of several pieces of information about the agent. The states are not images, so techniques like ...
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1answer
31 views

Compute state space from variables in Q-learning (RL)

I'm trying to use Q-learning, but I'm stuck because I don't know how to compute the state. Let's say, in my problem, there are the following variables, which I'm using to compute state: ...
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47 views

What's the best way to take a list of lists as DQN input?

I have my own environment for the DQN algorithm. In my environment, the state space is represented by a list of lists, where each sublist can be of different lengths. In my case, the length of the ...
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2answers
207 views

What is the relation between the context in contextual bandits and the state in reinforcement learning?

Conceptually, in general, how is the context being handled in contextual bandits (CB), compared to states in reinforcement learning (RL)? Specifically, in RL, we can use a function approximator (e.g. ...
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37 views

How should I model the state and action spaces for a problem where the goal is to draw a line between two points?

I have a problem where the goal is for the agent to draw a single line between two points on a $500 \times 500$ white image. I have built my DQN. For now, the output layer's size of the network is $[...
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2answers
86 views

Is there a natural way to define the terminal state from the MDP transition probabilities $p(s',r|s,a)$?

I'm learning the basics of RL and I'm struggling to understand the notion of terminal state in MDPs. To ask my question straightforwardly: is there a natural way to define the terminal state from the ...
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2answers
98 views

Reinforcement Learning algorithm with rewards dependent both on previous action and current action

Problem description: Suppose we have an environment, where a reward at time step $t$ is dependent not only on the current action, but also on previous action in the following way: if current action ==...
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1answer
69 views

How does replacing states with latent representations help RL agents?

I have seen many papers using autoencoders to replace images (states) with latent representations. Some of those methods have shown higher rewards using such techniques. However, I do not understand ...
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Is object-based representation of the observation space feasible?

I just started working on a DRL project from scratch. The state of each episode can be expressed as a state set $S=(S^A, S^B, S^C, S^D)$. Each subset is a feature set of a constituent component of the ...
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74 views

How to update the observation probabilities in a POMDP?

How can I update the observation probability for a POMDP (or HMM), in order to have a more accurate prediction model? The POMDP relies on observation probabilities that match an observation to a state....
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1answer
36 views

Does it make sense to include constant states into reinforcement learning formulation?

Does it make sense to incorporate constant states in the Markov Decision Process and employ a reinforcement learning algorithm to solve it? For instance, for applications of personalization, I would ...
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2answers
268 views

What happens when the agent faces a state that never before encountered?

I have a network with nodes and links, each of them with a certain amount of resources (that can take discrete values) at the initial state. At random time steps, a service is generated, and, based on ...
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1answer
68 views

Do I need to know in advance all possible number of states in Q-Learning?

In Q-learning, is it mandatory to know all possible states that can the agent may end up in? I have a network with 4 source nodes, 3 sink nodes, and 4 main links. The initial state is the status ...
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1answer
73 views

What constitutes a large space state (in Q-learning)?

I know this might be specific to different problems, but does anyone know if there is any rule of thumb or references on what constitutes a large state space? I know that, according to multiple papers,...
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74 views

Does the order in which the features are concatenated to create the state (or observation) matter?

I'm experimenting with an RL agent that interacts with the following environment. The learning algorithm is double DQN. The neural network represents the function from state to action. It's build with ...
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121 views

To solve chess with deep RL and MCTS, how should I represent the input (the state) to a neural network?

I'm wanting to build a NN that can create a policy for each possible state. I want to combine this with MCTS to eliminate randomness so when expansion occurs, I can get the probability of the move to ...
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1answer
66 views

What do the state features of KukaGymEnv represent?

I trying to use DDPG augmented with Hindsight Experience Replay (HER) on pybullet's KukaGymEnv. To formulate the feature vector for the goal state, I need to know what the features of the state of the ...
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45 views

When to do discretization to decrease the state/action space in RL?

When to do discretization to decrease the state/action space in RL? Can you give me some references that such a technique is used?
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1answer
244 views

It is possible to solve a problem with continuous action spaces and no states with reinforcement learning?

I want to use Reinforcement Learning to optimize the distribution of energy for a peak shaving problem given by a thermodynamical simulation. However, I am not sure how to proceed as the action space ...
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0answers
46 views

How does Hindsight Experience Replay cope with multiple goals?

What if there are multiple goals? For example, let's consider the bit-flipping environment as described in the paper HER with one small change: now, the goal is not some specific configuration, but ...
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3answers
1k views

What is a high dimensional state in reinforcement learning?

In the DQN paper, it is written that the state-space is high dimensional. I am a little bit confused about this terminology. Suppose my state is a high dimensional vector of length $N$, where $N$ is a ...
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2answers
11k views

How to define states in reinforcement learning?

I am studying reinforcement learning and the variants of it. I am starting to get an understanding of how the algorithms work and how they apply to an MDP. What I don't understand is the process of ...
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1answer
37 views

Which “assumptions” made about the state space are Russell and Norvig referring to in their book?

I am reading the cornerstone book, "Artificial Intelligence, A Modern Approach" by Stuart Russel, and Peter Norvig, and there is a passage in the book on page 98: The complexity results ...
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1answer
2k views

What are the state space and the state transition function in AI?

I'm studying for my AI final exam, and I'm stuck in the state space representation. I understand initial and goal states, but what I don't understand is the state space and state transition function. ...
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1answer
313 views

What techniques are used to make MDP discrete state space manageable?

Generating a discretized state space for an MDP (Markov Decision Process) model seems to suffer from the curse of dimensionality. Supposed my state has a few simple features: Feeling: Happy/Neutral/...