I'm now learning about reinforcement learning, but I just found the word "trajectory" in [this answer](https://datascience.stackexchange.com/a/24924/8432).

However, I'm not sure what it means. I read a few books on the Reinforcement Learning but none of them mentioned it. Usually these introductionary books mention agent, environment, action, policy, and reward, but not "trajectory".

So, what does it mean? 
According to [this answer](https://www.quora.com/In-the-context-of-reinforcement-learning-what-is-the-difference-between-a-trajectory-and-a-policy-Also-what-is-the-difference-between-trajectory-optimization-and-policy-optimization) over Quora:

> In reinforcement learning terminology, a trajectory $\tau$ is the path of the agent through the state space up until the horizon $H$. The goal of an on-policy algorithm is to maximize the expected reward of the agent over trajectories.

Does it mean that the "trajectory" is the total path from the current state the agent is in to the final state (terminal state) that the episode finishes at? Or is it something else? (I'm not sure what the "horizon" mean, either).