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Adaptive Teaching in Heterogeneous Agents: Balancing Surprise in Sparse Reward Scenarios

Adaptive Teaching in Heterogeneous Agents: Balancing Surprise in Sparse Reward Scenarios

Emma Clark
Kanghyun Ryu
Negar Mehr
ArXivPDFHTML

Papers citing "Adaptive Teaching in Heterogeneous Agents: Balancing Surprise in Sparse Reward Scenarios"

4 / 4 papers shown
Title
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models
Kanghyun Ryu
Qiayuan Liao
Zhongyu Li
Koushil Sreenath
Negar Mehr
Negar Mehr
LM&Ro
239
3
0
27 Sep 2024
RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch
RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch
Haruki Nishimura
Negar Mehr
Adrien Gaidon
Mac Schwager
40
13
0
16 Oct 2020
Proximal Policy Optimization Algorithms
Proximal Policy Optimization Algorithms
John Schulman
Filip Wolski
Prafulla Dhariwal
Alec Radford
Oleg Klimov
OffRL
183
18,685
0
20 Jul 2017
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments
Yi Sun
Faustino J. Gomez
Jürgen Schmidhuber
53
163
0
29 Mar 2011

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