Reinforcement Learning
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Neural Information Processing Systems http://nips.cc/
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e.g.
Planning with Goal-Conditioned Policies
Soroush Nasiriany, Vitchyr Pong, Steven Lin, Sergey Levine
Can we utilize reinforcement learning to automatically form the abstractions needed for planning, thus obtaining the best of both approaches?
Constrained Reinforcement Learning Has Zero Duality Gap
Santiago Paternain, Luiz Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro
Neural Information Processing Systems http://nips.cc/
Extending the reward structure in reinforcement learning: an interview with Tanmay Ambadkar
In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Tanmay Ambadkar is researching the reward structure in reinforcement learning, with the goal of providing generalizable solutions that can provide robust guarantees and are easily deployable. We caught up with Tanmay to find out more about his research, and in particular, the constrained reinforcement learning framework he has been working on. Tell us a bit about your PhD - where are you studying, and what is the topic of your research? I am a 4th year PhD candidate at The Pennsylvania State University, PA, USA.
Reinforcement learning applied to autonomous vehicles: an interview with Oliver Chang
In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We caught up with Oliver Chang whose research interests span deep reinforcement learning, autonomous vehicles, and explainable AI. We found out more about some of the projects he's worked on so far, what drew him to the field, and what future AI directions he's excited about. Could you give us a quick introduction to who you are, where you're studying, and the topic of your research? I'm specializing in reinforcement learning applied to autonomous vehicles and UAVs.
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
Neural Information Processing Systems http://nips.cc/
Amortized Active Causal Induction with Deep Reinforcement Learning
Our design policy successfully achieves amortized intervention design on the distribution of the training environment while also generalizing well to distribution shifts in test-time design environments.
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
Erwan Lecarpentier, Emmanuel Rachelson
This work tackles the problem of robust planning in non-stationary stochastic environments.