REVIEW 5 cited by
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited. However, in many practical applications, the situation is reversed: an agent may have access to large amounts of undirected offline experience data, while access to the online environment is severely limited. In this work, we focus on this offline setting. Our main insight is that, when presented with offline data composed of a variety of behaviors, an effective way to leverage this data is to extract a continuous space of recurring and temporally extended primitive behaviors before using these primitives for downstream task learning. Primitives extracted in this way serve two purposes: they delineate the behaviors that are supported by the data from those that are not, making them useful for avoiding distributional shift in offline RL; and they provide a degree of temporal abstraction, which reduces the effective horizon yielding better learning in theory, and improved offline RL in practice. In addition to benefiting offline policy optimization, we show that performing offline primitive learning in this way can also be leveraged for improving few-shot imitation learning as well as exploration and transfer in online RL on a variety of benchmark domains. Visualizations are available at https://sites.google.com/view/opal-iclr
Forward citations
Cited by 5 Pith papers
-
Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
A taxonomy of robot learning on a weights-versus-skills axis, with a five-rung self-improvement ladder whose top cell (feedback plus memory plus search) holds only a few recent systems.
-
Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation
An imitation-learning system that segments demonstrations into VLM-labeled atomic skills, aligns them with contrastive learning, and uses keypose prediction to chain skills, outperforming prior baselines in multi-task...
-
Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach
A two-stage RL framework learns value-function envelopes from offline data and uses them to shape online exploration, yielding regret bounds that improve as offline data grows.
-
Learning Temporal Abstractions via Variational Homomorphisms in Option-Induced Abstract MDPs
A variational option-critic algorithm with latent option embeddings and an implicit chain-of-thought cold-start is presented; the central optimality-preservation proof has a gap and some reported benchmark wins are in...
-
Behavioral Exploration: Learning to Explore via In-Context Adaptation
A coverage-conditioned behavioral cloning policy adapts in-context to its own history, making robots explore new expert-like behaviors online without online reinforcement learning.
Discussion (0). Continue with ORCID to comment.