2 citations · 4 across the 8 of their papers we have counts for
8 papers
Which Rewards Matter? Reward Selection for Reinforcement Learning under Limited Feedback
Shreyas Chaudhari, Renhao Zhang, Philip S. Thomas +1
The ability of reinforcement learning algorithms to learn effective policies is determined by the rewards available during training. However, for practical problems, obtaining larg…
Reinforcement Learning for Quantum Network Control with Application-Driven Objectives
Guo Xian Yau, Alexandra Burushkina, Francisco Ferreira da Silva +3
Optimized control of quantum networks is essential for enabling distributed quantum applications with strict performance requirements. In near-term architectures with constrained h…
Qualia Optimization
Philip S. Thomas
This report explores the speculative question: what if current or future AI systems have qualia, such as pain or pleasure? It does so by assuming that AI systems might someday poss…
Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation
Shreyas Chaudhari, Ameet Deshpande, Bruno Castro da Silva +1
Evaluating policies using off-policy data is crucial for applying reinforcement learning to real-world problems such as healthcare and autonomous driving. Previous methods for off-…
Position: Benchmarking is Limited in Reinforcement Learning Research
Scott M. Jordan, Adam White, Bruno Castro da Silva +2
Novel reinforcement learning algorithms, or improvements on existing ones, are commonly justified by evaluating their performance on benchmark environments and are compared to an e…
ICU-Sepsis: A Benchmark MDP Built from Real Medical Data
Kartik Choudhary, Dhawal Gupta, Philip S. Thomas
We present ICU-Sepsis, an environment that can be used in benchmarks for evaluating reinforcement learning (RL) algorithms. Sepsis management is a complex task that has been an imp…
From Past to Future: Rethinking Eligibility Traces
Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari +3
In this paper, we introduce a fresh perspective on the challenges of credit assignment and policy evaluation. First, we delve into the nuances of eligibility traces and explore ins…
Behavior Alignment via Reward Function Optimization
Dhawal Gupta, Yash Chandak, Scott M. Jordan +2
Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task. This is challenging since it requires the identif…