activity
20172026
most citedOpen Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

95 citations · 378 across the 58 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.LG2021★ 2 cited

Learning Multimodal Rewards from Rankings

Vivek Myers, Erdem Bıyık, Nima Anari +1

Learning from human feedback has shown to be a useful approach in acquiring robot reward functions. However, expert feedback is often assumed to be drawn from an underlying unimoda…

cs.LG2021★ 1 cited

Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams

Erdem Bıyık, Anusha Lalitha, Rajarshi Saha +2

When humans collaborate with each other, they often make decisions by observing others and considering the consequences that their actions may have on the entire team, instead of g…

cs.RO2021★ 6 cited

Learning Reward Functions from Scale Feedback

Nils Wilde, Erdem Bıyık, Dorsa Sadigh +1

Today's robots are increasingly interacting with people and need to efficiently learn inexperienced user's preferences. A common framework is to iteratively query the user about wh…

cs.LG2021★ 5 cited

APReL: A Library for Active Preference-based Reward Learning Algorithms

Erdem Bıyık, Aditi Talati, Dorsa Sadigh

Reward learning is a fundamental problem in human-robot interaction to have robots that operate in alignment with what their human user wants. Many preference-based learning algori…

cs.MA2021

Emergent Prosociality in Multi-Agent Games Through Gifting

Woodrow Z. Wang, Mark Beliaev, Erdem Bıyık +3

Coordination is often critical to forming prosocial behaviors -- behaviors that increase the overall sum of rewards received by all agents in a multi-agent game. However, state of…

cs.MA2021

Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy

Erdem Bıyık, Daniel A. Lazar, Ramtin Pedarsani +1

Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle plato…

cs.RO2021★ 2 cited

Preference-based Learning of Reward Function Features

Sydney M. Katz, Amir Maleki, Erdem Bıyık +1

Preference-based learning of reward functions, where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as autonomous drivin…