3 papers
cs.LG2025
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…
cs.RO2024
Comp-LTL: Temporal Logic Planning via Zero-Shot Policy Composition
Taylor Bergeron, Zachary Serlin, Kevin Leahy
This work develops a zero-shot mechanism, Comp-LTL, for an agent to satisfy a Linear Temporal Logic (LTL) specification given existing task primitives trained via reinforcement lea…
cs.LG2024
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…