The document discusses various types of environments in the context of agents, distinguishing between fully observable and partially observable environments, deterministic and stochastic environments, and episodic and sequential environments. It further categorizes environments as static or dynamic, discrete or continuous, single-agent or multi-agent, and known or unknown, providing examples for each type. The content is aimed at helping individuals prepare for academic subjects related to computer science and artificial intelligence.

1. Types Of Environment
6

2. Environment
An Environment is Everything in the
world which surrounds the agent,
but it is not a part of an agent itself.

3. Fully Observable vs Partially Observable
An environment is called Fully Observable when the information received by an
agent at any point of time is sufficient to make the optimal decision. An environment
is called Partially Observable when the agent needs a memory in order to make the
best possible decision.

4. Deterministic vs Stochastic
An environment is called Deterministic when agent's actions
uniquely determine the outcome. An environment is
called Stochastic when an agent's actions don't uniquely
determine the outcome.

5. Episodic vs Sequential
In an episodic environment, there is a series of one-shot
actions, and only the current percept is required for the
action. However, in Sequential environment, an agent
requires memory of past actions to determine the next best
actions.

6. Static vs Dynamic
If the environment can change while an agent is deliberating,
then we say the environment is dynamic for that agent;
otherwise, it is static.

7. Discrete vs Continuous
Discrete environments are those on which a finite [although
arbitrarily large] set of possibilities can drive the outcome of the
task. Continuous environments rely on unknown and rapidly
changing data sources.

8. Single Agent vs Multiagent
If only one agent is involved in an environment and operating by itself
then such an environment is called single agent environment.
However, if multiple agents are operating in an environment, then
such an environment is called a multi-agent environment.

9. Known vs Unknown
 In a known environment, the results for all actions
are known to the agent. While in unknown environment,
agent needs to learn how it works in order to perform an
action.
 In known environment , the outcomes for all actions are given.
Examples : Solitaire, card games.
 If the environment is unknown , the agent will have to learn
how it work in order to make good decisions.
Example: New video game.

11. Thanks For
Watching
Reference:
Artificial Intelligence
A Modern Approach Third Edition
Peter Norvig and Stuart J. Russell
Next Topic:
The Structure of Agents.
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