44 citations · 46 across the 8 of their papers we have counts for
4 papers · 1 filter
SPLASH! Sample-efficient Preference-based inverse reinforcement learning for Long-horizon Adversarial tasks from Suboptimal Hierarchical demonstrations
Peter Crowley, Zachary Serlin, Tyler Paine +3
Inverse Reinforcement Learning (IRL) presents a powerful paradigm for learning complex robotic tasks from human demonstrations. However, most approaches make the assumption that ex…
Accelerating Proximal Policy Optimization Learning Using Task Prediction for Solving Environments with Delayed Rewards
Ahmad Ahmad, Mehdi Kermanshah, Kevin Leahy +6
In this paper, we tackle the challenging problem of delayed rewards in reinforcement learning (RL). While Proximal Policy Optimization (PPO) has emerged as a leading Policy Gradien…
Safety-Aware Task Composition for Discrete and Continuous Reinforcement Learning
Kevin Leahy, Makai Mann, Zachary Serlin
Compositionality is a critical aspect of scalable system design. Reinforcement learning (RL) has recently shown substantial success in task learning, but has only recently begun to…
CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications
Wenliang Liu, Kevin Leahy, Zachary Serlin +1
In this paper, we propose a learning-based framework to simultaneously learn the communication and distributed control policies for a heterogeneous multi-agent system (MAS) under c…