Timezone: »
An artificial general intelligence (AGI) agent is capable of achieving general goals. An agent that reasons about generality is complicated. The world the AGI is interacting with, however, is much more complicated than the agent itself. Further, the agent only observes a part of the world at a time and thus needs to construct its own summary of the past and the summary is the agent’s subjective state. All components that the agent has, except the one that generates the agent’s state, take the agent’s state as input and generate desired outputs. What components the agent should maintain and how the specific components interact with each other are two fundamental questions. Specific questions arise from these two fundamental questions. For example, what are good agent states and what are bad ones? What should the world model take and produce? Are sub-tasks necessary? What sub-tasks are good and what are bad? These questions are about designing architecture and identifying the purposes of each component in the architecture, rather than specific ways to implement each component. Our social welcomes everyone who is interested in brainstorming such an architecture design.
Author Information
Yi Wan (University of Alberta)
Alex Ayoub (University of Alberta / Amii)
More from the Same Authors
-
2022 Poster: Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods »
Yi Wan · Ali Rahimi-Kalahroudi · Janarthanan Rajendran · Ida Momennejad · Sarath Chandar · Harm van Seijen -
2022 Spotlight: Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods »
Yi Wan · Ali Rahimi-Kalahroudi · Janarthanan Rajendran · Ida Momennejad · Sarath Chandar · Harm van Seijen -
2021 Poster: Average-Reward Off-Policy Policy Evaluation with Function Approximation »
Shangtong Zhang · Yi Wan · Richard Sutton · Shimon Whiteson -
2021 Spotlight: Average-Reward Off-Policy Policy Evaluation with Function Approximation »
Shangtong Zhang · Yi Wan · Richard Sutton · Shimon Whiteson -
2021 Social: RL Social »
Dibya Ghosh · Hager Radi · Derek Li · Alex Ayoub · Erfan Miahi · Rishabh Agarwal · Charline Le Lan · Abhishek Naik · John D. Martin · Shruti Mishra · Adrien Ali Taiga -
2021 Poster: Randomized Exploration in Reinforcement Learning with General Value Function Approximation »
Haque Ishfaq · Qiwen Cui · Viet Nguyen · Alex Ayoub · Zhuoran Yang · Zhaoran Wang · Doina Precup · Lin Yang -
2021 Spotlight: Randomized Exploration in Reinforcement Learning with General Value Function Approximation »
Haque Ishfaq · Qiwen Cui · Viet Nguyen · Alex Ayoub · Zhuoran Yang · Zhaoran Wang · Doina Precup · Lin Yang -
2021 Poster: Learning and Planning in Average-Reward Markov Decision Processes »
Yi Wan · Abhishek Naik · Richard Sutton -
2021 Spotlight: Learning and Planning in Average-Reward Markov Decision Processes »
Yi Wan · Abhishek Naik · Richard Sutton -
2021 Social: Continuing (Non-episodic) RL Problems »
Yi Wan -
2020 Poster: Model-Based Reinforcement Learning with Value-Targeted Regression »
Alex Ayoub · Zeyu Jia · Csaba Szepesvari · Mengdi Wang · Lin Yang