Sorry for the lateness, please note this talk is a normal tea talk and it is *tomorrow!* If you want to meet with the speaker, please email me and I'll set it up!
This week we have *Bradly Stadie* from *UC Berkeley* giving a talk on *Wednesday Feb 21* at *3:00PM* in room *AA1360*.
See you there! Michael
*TITLE* Towards Quickly Learning Agents
*KEYWORDS *Meta-learning, Reinforcement Learning, Imitation Learning
*ABSTRACT*We consider the problems of meta reinforcement learning, wherein the goal is to train agents that can learn new tasks quickly. We suggest 5 major sub-problems that are crucial for enabling quickly learning agents: Exploration, Imitation, Hierarchy/Planning, Credit Assignment, and Control.
The talk will examine the sub-problems of exploration and imitation and their interplay with quickly learning agents in more depth. In particular, we examine:
1) Third Person Imitation: which enables agents to imitate other agents that differ significantly from themselves. 2) One Shot Imitation: which enables a robot to see a single video of a demonstration and copy the demonstrated behavior. 3) E-MAML: a meta-RL algorithm that achieves significantly better exploration than previous meta-RL algorithms.
*BIO* Bradly Stadie is a PhD student at UC Berkeley in Pieter Abbeel’s lab. Previously, he spent 2 years working at Open AI under Ilya Sutskever. Bradly’s work has focused on the fields of exploration, imitation, and meta learning with applications to games, robotics, and causal inference. Prior to his interest in AI, Bradly studied mathematics at the University of Chicago, where his work focused on representation theory of locally compact groups and stochastic differential equations.
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*CORRECTION*: the room is Pavilion Claire McNicoll Z220
On Tue, Feb 20, 2018 at 12:14 PM Michael Noukhovitch mnoukhov@gmail.com wrote:
Sorry for the lateness, please note this talk is a normal tea talk and it is *tomorrow!* If you want to meet with the speaker, please email me and I'll set it up!
This week we have *Bradly Stadie* from *UC Berkeley* giving a talk on *Wednesday Feb 21* at *3:00PM* in room *AA1360*.
See you there! Michael
*TITLE* Towards Quickly Learning Agents
*KEYWORDS *Meta-learning, Reinforcement Learning, Imitation Learning
*ABSTRACT*We consider the problems of meta reinforcement learning, wherein the goal is to train agents that can learn new tasks quickly. We suggest 5 major sub-problems that are crucial for enabling quickly learning agents: Exploration, Imitation, Hierarchy/Planning, Credit Assignment, and Control.
The talk will examine the sub-problems of exploration and imitation and their interplay with quickly learning agents in more depth. In particular, we examine:
- Third Person Imitation: which enables agents to imitate other agents
that differ significantly from themselves. 2) One Shot Imitation: which enables a robot to see a single video of a demonstration and copy the demonstrated behavior. 3) E-MAML: a meta-RL algorithm that achieves significantly better exploration than previous meta-RL algorithms.
*BIO* Bradly Stadie is a PhD student at UC Berkeley in Pieter Abbeel’s lab. Previously, he spent 2 years working at Open AI under Ilya Sutskever. Bradly’s work has focused on the fields of exploration, imitation, and meta learning with applications to games, robotics, and causal inference. Prior to his interest in AI, Bradly studied mathematics at the University of Chicago, where his work focused on representation theory of locally compact groups and stochastic differential equations.
Starting now!
2018-02-20 12:20 GMT-05:00 Michael Noukhovitch mnoukhov@gmail.com:
*CORRECTION*: the room is Pavilion Claire McNicoll Z220
On Tue, Feb 20, 2018 at 12:14 PM Michael Noukhovitch mnoukhov@gmail.com wrote:
Sorry for the lateness, please note this talk is a normal tea talk and it is *tomorrow!* If you want to meet with the speaker, please email me and I'll set it up!
This week we have *Bradly Stadie* from *UC Berkeley* giving a talk on *Wednesday Feb 21* at *3:00PM* in room *AA1360*.
See you there! Michael
*TITLE* Towards Quickly Learning Agents
*KEYWORDS *Meta-learning, Reinforcement Learning, Imitation Learning
*ABSTRACT*We consider the problems of meta reinforcement learning, wherein the goal is to train agents that can learn new tasks quickly. We suggest 5 major sub-problems that are crucial for enabling quickly learning agents: Exploration, Imitation, Hierarchy/Planning, Credit Assignment, and Control.
The talk will examine the sub-problems of exploration and imitation and their interplay with quickly learning agents in more depth. In particular, we examine:
- Third Person Imitation: which enables agents to imitate other agents
that differ significantly from themselves. 2) One Shot Imitation: which enables a robot to see a single video of a demonstration and copy the demonstrated behavior. 3) E-MAML: a meta-RL algorithm that achieves significantly better exploration than previous meta-RL algorithms.
*BIO* Bradly Stadie is a PhD student at UC Berkeley in Pieter Abbeel’s lab. Previously, he spent 2 years working at Open AI under Ilya Sutskever. Bradly’s work has focused on the fields of exploration, imitation, and meta learning with applications to games, robotics, and causal inference. Prior to his interest in AI, Bradly studied mathematics at the University of Chicago, where his work focused on representation theory of locally compact groups and stochastic differential equations.
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