Belangrijkste concepten
COInS algorithm introduces interaction-guided skill acquisition in hierarchical RL, improving sample efficiency and transferability.
Samenvatting
The content discusses the COInS algorithm, focusing on controllability in factored domains to identify task-agnostic skills. It addresses the limitations of traditional RL methods by leveraging interactions between state factors to improve sample efficiency and transferability. The algorithm is evaluated on challenging tasks like Breakout and Robot Pushing, showcasing significant improvements over standard RL baselines.
Introduction:
Reinforcement learning struggles with high data requirements and brittle generalization.
Hierarchical RL decomposes tasks into skills for improved efficiency.
COInS algorithm focuses on controllability in factored domains.
Data Extraction:
"We evaluate COInS on a robotic pushing task with obstacles—a challenging domain where other RL and HRL methods fall short."
"We also demonstrate the transferability of skills learned by COInS, using variants of Breakout, a common RL benchmark."
Related Work:
Reward-free vs reward-based skill learning methods compared.
COInS shares similarities with HyPE but uses interactions for skill acquisition.
Chain of Interaction Skills:
COInS iteratively learns pairwise skills based on interactions.
Detects interactions using Granger Causality test.
Builds a chain of goal-based skills for efficient learning and transfer.
Experiments:
COInS demonstrates superior sample efficiency in Breakout and Robot Pushing tasks.
Baselines struggle with credit assignment and complex reward structures.
Transfer:
COInS shows successful skill transfer to variants of Breakout with challenging reward structures.
Overall Performance:
COInS outperforms baselines in terms of sample efficiency, performance, and transferability.
Statistieken
COInS uses Granger-causal tests to detect interactions between state factors.
COInS shows 2-3x improvement in both sample efficiency and final performance compared to standard RL baselines.
Citaten
"COInS focuses on controllability in factored domains to identify task-agnostic skills."
"We evaluate COInS on a robotic pushing task with obstacles—a challenging domain where other RL and HRL methods fall short."