An @RL_conference workshop for examining conceptual frameworks in reinforcement learning.

Joined March 2024
10 Photos and videos
Our deadline has been extended to May 29 AoE!
Reminder that the deadline for the Finding the Frame workshop @RL_Conference is coming up on May 22 AoE ⏰ We're excited to read your submissions reflecting on the philosophy, practice, and formalisms of reinforcement learning! 🔗 More details: sites.google.com/view/findin…
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Reminder that the deadline for the Finding the Frame workshop @RL_Conference is coming up on May 22 AoE ⏰ We're excited to read your submissions reflecting on the philosophy, practice, and formalisms of reinforcement learning! 🔗 More details: sites.google.com/view/findin…
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Finding the Frame will be back at @RL_Conference 2026 with a fantastic speaker lineup! We welcome submissions that reflect on the philosophy, practice, and formalisms of reinforcement learning. 📅Submission deadline: May 22, 2026 (AoE) More details: sites.google.com/view/findin…
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We're thrilled to announce Finding the Frame is back for the third time @RL_Conference 2026!🎉 Call for papers coming soon 🚀
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Thanks to everyone who joined us for another great workshop! 🥳 This year we once again asked our panelists to share a paper or book that heavily influenced their perspective 📚 check out their recommendations here! sites.google.com/view/findin…
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Completing our fantastic lineup of invited talks is Dr. @clarelyle (Deepmind) who will speak on Beyond Optimality: Designing Open-Ended RL Agents. Abstract in 🧵
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such as the distinction between the agent and environment, the stationarity of the environment transition dynamics, and the external source of reward, influence the types of behaviour we can expect to observe in our agents.
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We will conclude with a discussion of alternative assumptions which might be more friendly to the development of agents capable of the open-ended, iterative, and creative intelligent behaviours necessary for pushing the frontier of human knowledge.
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Professor @mark_ho_ (NYU) will give an invited talk on Why Having a Mind Might be Useful 🧠 Abstract in 🧵
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Reinforcement learning studies systems that generate reward-maximizing behavior. Where does the idea of a mind fit into this picture, if it’s even needed? Should we be behaviorists, cognitivists, or something in between?
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I will discuss some of the reasons why RL might want to take minds seriously, despite some of the challenges involved in studying them.
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Professor George Konidaris (Brown University) will be giving an invited talk titled: Agents Must Learn Their Own Frames. Abstract in 🧵
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and that therefore 3) a critical component for achieving general intelligence is the ability to autonomously construct task-specific frames, which can be achieved by learning mutually-compatible observation and action abstractions.
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Finally, I will briefly discuss how 4) a reinforcement-learning agent able to do so can access the rest of AI.
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We have a really exciting lineup of invited speakers this year 🔥 Kicking us off we have Prof. Erin Talvitie (Harvey Mudd College), whose talk is titled: 20 Years of Asking the Wrong Questions in Model-based reinforcement learning. Abstract in 🧵
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And yet, making sustained, robust progress toward this goal has proved surprisingly, infuriatingly difficult. We'll take a high-level tour of some garden paths and blind alleys that have caused useful shifts in my own frame,
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ending on the hopeful note that maybe, just maybe, some of the right questions are starting to come into view.
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