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What is an AI agent in the simplest terms?
An AI system that perceives its environment, makes decisions, and takes actions to achieve goals—like a robot that senses obstacles and navigates around them.
What's the difference between an AI agent and a regular AI model?
An agent acts in an environment and receives feedback; a model just makes predictions. Agents learn from consequences of their actions over time.
What is the agent-environment loop?
The cycle where an agent observes the current state, chooses an action, the environment responds with a new state and reward, and the agent learns from this feedback.
In reinforcement learning, what is a policy?
A strategy or rule that maps observed states to actions. It tells the agent what to do in each situation (can be deterministic or probabilistic).
What does a value function estimate in reinforcement learning?
The expected cumulative reward an agent will receive from a given state (or state-action pair) following its policy—essentially, 'how good is this position?'
Explain the explore-exploit trade-off intuitively.
An agent must balance trying new actions (explore) to find better rewards versus repeating known good actions (exploit). Too much of either is suboptimal.
What is a Markov Decision Process (MDP)?
A mathematical framework for agent decision-making: states, actions, transition probabilities, and rewards where the next state depends only on the current state and action (memoryless property).
How does the Bellman equation work conceptually?
It breaks down the value of a state into immediate reward plus the discounted value of the next state, enabling agents to learn optimal decisions recursively: V(s) = E[R + γV(s')]
What's the key difference between model-based and model-free RL?
Model-based agents learn the environment's dynamics (how actions cause state changes) and plan ahead; model-free agents learn value/policy directly through trial-and-error without understanding the environment.
In modern LLM agents, what role does the language model play?
The LLM acts as the decision-maker and planner—it observes environment state (text/tool outputs), reasons about goals, and generates actions (API calls, tool use, or responses) in natural language.
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