Agent Environment in AI
Table of Content:
What is agent environment in AI?
An environment in the context of artificial intelligence is the external context or surroundings in which an agent operates. It encompasses everything in the surroundings of the agent but is not considered a part of the agent itself. The environment represents the situation or context in which an agent exists, providing the agent with stimuli to sense and opportunities to act upon. It is imp
What are the features of Environment?
According to Russell and Norvig, an environment might have a variety of characteristics from the perspective of an agent:
- Fully observable vs Partially Observable
- Static vs Dynamic
- Discrete vs Continuous
- Deterministic vs Stochastic
- Single-agent vs Multi-agent
- Episodic vs sequential
- Known vs Unknown
- Accessible vs Inaccessible
1. Fully observable vs Partially Observable:
- A fully observable environment is one in which an agent sensor may perceive or access the entire state of an environment at any given time; otherwise, it is partially observable.
- It's simple to create a completely observable environment because there's no need to keep track of the world's past.
- Unobservable environments are those in which an agent has no sensors in all environments.
2. Deterministic vs Stochastic:
- If an agent's current state and selected action can perfectly predict the forthcoming state of the environment, then such environment is a deterministic environment.
- An agent cannot entirely control a stochastic environment because it is unpredictable in nature.
- Agents do not need to be concerned about uncertainty in a deterministic, fully observable world.
3. Episodic vs Sequential:
- In an episodic environment, there are a succession of one-shot actions that just require the present percept.
- In a Sequential context, however, an agent must remember previous acts in order to decide the next best action.
4. Single-agent vs Multi-agent:
- The term "single agent environment" refers to an environment in which just one agent is present and operates independently.
- A multi-agent environment, on the other hand, is one in which numerous agents are functioning in the same space.
- The issues of agent design in a multi-agent environment differ from those in a single-agent environment.
5. Static vs Dynamic:
- If the environment can vary while an agent is deliberating, it is referred to as a dynamic environment; otherwise, it is referred to as a static environment.
- Static settings are simple to deal with because an agent does not need to keep glancing around while making a decision.
- Agents, on the other hand, in a dynamic environment must continuously glancing about at each action.
6. Discrete vs Continuous:
- A discrete environment is one in which there are a finite number of percepts and actions that can be performed within it, whereas a continuous environment is one in which there are an infinite number of percepts and actions that may be performed within it.
- A chess game takes place in a discrete context since there are only so many moves that may be made.
- A continuous environment is exemplified by a self-driving automobile.
7. Known vs Unknown:
- The terms "known" and "unknown" do not refer to features of the environment, but rather to an agent's state of knowledge when performing an action.
- The effects of all actions are known to the agent in a known environment. In order to perform an action in an unfamiliar environment, the agent must first learn how it operates.
- A known environment could be partially observable whereas an unknown environment could be entirely observable.
8. Accessible vs Inaccessible:
- If an agent can acquire complete and correct knowledge about the state's environment, it is referred to as an accessible environment; otherwise, it is referred to as an inaccessible environment.
- An accessible environment is an empty room whose condition may be characterized by its temperature.
- An example of an inaccessible environment is information regarding a global incident.