4 accepted papers
Deep reinforcement learning is a promising approach to learning policies in unstructured environments. Due to its sample inefficiency, though, deep RL applications have primarily focused on simulated environments. In this work, we demonstrate that the recent advancements in machine learning algorith…
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running. However, these capabilities bring with them difficult control problems, and designing controllers for highly agile dynamic motions remains a substantial challenge for ro…
Meta-reinforcement learning (RL) methods can meta-train policies that adapt to new tasks with orders of magnitude less data than standard RL, but meta-training itself is costly and time-consuming. If we can meta-train on offline data, then we can reuse the same static dataset, labeled once with rewa…
Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet easy enough to provide. Human-in-the-loop RL methods allow practitioners to instead interactively teach agents through tail…