Sample Complexity of Equivariant Reinforcement Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2605. 23415v2 Announce Type: replace Abstract: Reinforcement learning has long struggled with poor sample efficiency.
The paper introduces a reinforcement learning framework that uses groupoids to model local, state-dependent symmetries, allowing agents to discover equivalence structures during interaction. By maintaining orbit representatives and transporters that map raw states to canonical forms, learning and decision-making occur in a symmetry-reduced space while preserving local distinctions. Experiments show that this approach improves sample efficiency and convergence in dense, large-scale environments with partial symmetries, outperforming standard Q‑learning.
The paper proposes using category theory to structure reinforcement learning in high‑dimensional, partially observable environments. By partitioning the state space into equivalence classes (symmetry orbits) and treating each class as a groupoid with a canonical representative, the agent can share learning across similar states, reducing redundancy and improving sample efficiency. Experiments on partially observable benchmarks show that this orbit‑based partitioning consistently enhances performance in environments with latent symmetry.
arXiv:2607. 11624v1 Announce Type: cross Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency.
Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics priors in the learning process.
arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.