Agents2
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The document discusses different types of environments that an agent may interact with. It describes environments as being fully observable or partially observable, deterministic or stochastic, episodic or sequential, static or dynamic, discrete or continuous, and single agent or multiagent. Fully observable means the agent can perceive the complete state, while partially observable means relevant aspects may be unknown. Deterministic means the next state is completely determined, while stochastic means it depends on chance.
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Agents2
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- 13. lecture 2 13 Environment Types * Fully observable vs. partially observable. If an agent's sensors give it access to the complete state of the environment at each point in time, then we say that the task environment is fully observable. A task environment is effectively fully observable if the sensors detect all aspects that are relevant to the choice of action; relevance, in turn, depends on the performance measure. * Deterministic vs. stochastic. If the next state of the environment is completely determined by the current state and the action executed by the agent, then we say the environment is deterministic; otherwise, it is stochastic.
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- 16. lecture 2 16 Environment Types Episodic vs. sequential. In an episodic task environment, the agent's experience is divided into atomic episodes. Each episode consists of the agent perceiving and then performing a single action. Crucially, the next episode does not depend on the actions taken in previous episodes. * 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.
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- 19. lecture 2 19 Environment Types * Discrete vs. continuous. The discrete/continuous distinction can be applied to the state of the environment, to the way time is handled, and to the percepts and actions of the agent. * Single agent vs. multiagent. The distinction between single-agent and multiagent environments may seem simple enough
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Editor's Notes
- #7: Omnisient 悋 悋惺 Clairvoyant 惡惺惆 悋惴惘
- #14: .
- #24: Simple reflex agents The simplest kind of agent is the simple reflex agent. These agents select actions on the basis of the current percept, ignoring the rest of the percept history. The agent will work only if the correct decision can be made on the basis of only the current percept-that is, only if the environrnents fully observable.
- #26: The most effective way to handle partial observability is for the agent to keep track of the part of the world it can't see now. That is, the agent should maintain some sort of internal state that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state.
- #27: Knowing about the current state of the environment is not always enough to decide what to do. For example, at a road junction, the taxi can turn left, turn right, or go straight on. The correct decision depends on where the taxi is trying to get to.
- #28: Goals alone are not really enough to generate high-quality behavior in most environments. For example, there are many action sequences that will get the taxi to its destination (thereby achieving the goal) but some are quicker, safer, more reliable, or cheaper than others. A utility function maps a state (or a sequence of states) onto a real number, which describes the associated degree of happiness.