CSUMB Rotaract General Meeting: 05/04/2015

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CSUMBRotaract

This is the presentation for the general meeting on 05/04/2015.

Presentations & Public Speaking

Rotaract ‘14-’15 Goals
End Polio
Goal: $600
March of Dimes
Goal:$400
Hours
Goal:1,000
Completed:
866.8 Hours!!!
Raised:$916.44Raised:$412.6
Congradulations!!!

Rotaract ‘15-’16 Goals
● What do you want to see from Rotaract next
year?
- Types of service events
- Socials
- Any specific goals
● How can we improve the meetings?
- Ideas for Icebreakers

Contact Information
- Join our Facebook group CSUMB Rotaract
- Email us… rotaract@csumb.edu
- Visit our website… csumbrotaract.org
CSUMB Rotaract Club

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Despite remarkable successes in various domains such as robotics and games, Reinforcement Learning (RL) still struggles with exploration inefficiency. For example, in hard Atari games, state-of-the-art agents often require billions of trial actions, equivalent to years of practice, while a moderately skilled human player can achieve the same score in just a few hours of play. This contrast emerges from the difference in exploration strategies between humans, leveraging memory, intuition and experience, and current RL agents, primarily relying on random trials and errors. This tutorial reviews recent advances in enhancing RL exploration efficiency through intrinsic motivation or curiosity, allowing agents to navigate environments without external rewards. Unlike previous surveys, we analyze intrinsic motivation through a memory-centric perspective, drawing parallels between human and agent curiosity, and providing a memory-driven taxonomy of intrinsic motivation approaches. The talk consists of three main parts. Part A provides a brief introduction to RL basics, delves into the historical context of the explore-exploit dilemma, and raises the challenge of exploration inefficiency. In Part B, we present a taxonomy of self-motivated agents leveraging deliberate, RAM-like, and replay memory models to compute surprise, novelty, and goal, respectively. Part C explores advanced topics, presenting recent methods using language models and causality for exploration. Whenever possible, case studies and hands-on coding demonstrations. will be presented.

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