TEXPLORE is a Reinforcement Learning (RL) algorithm, or a method
for agents to learn to perform sequential decision making tasks
through interaction with their environment. This research is
focused on applying RL to more real-world problems, particularly
learning on robots. Enabling robots to learn will make them
generally more useful, as they will not require pre-programming
for every task and environment. In RL, an agent is in some state
in the world (e.g. a particular chess board configuration or a
location in a city) and has some set of actions it can take
(e.g. chess moves, turns at an intersection). Upon taking an
action, it reaches a new state, and receives a scalar reward
(e.g. +1 for winning the chess game, -1 for losing, and 0
otherwise; or minus the time each road segment took). The goal
of the agent is to learn which action to take in each state to
maximize its reward over time. There are a number of issues with
applying RL to real-world problems such as robots. First,
learning must happen with a