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Meta-Reinforcement Learning via Exploratory Task Clustering
Machine Learning · Submitted 15 Feb 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experiments on various MuJoCo tasks showed the proposed method can unravel cluster structures effectively in both rewards and state dynamics, proving strong advantages against a set of state-of-the-art baselines.
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The abstract, sentence by sentence
Meta-reinforcement learning (meta-RL) aims to quickly solve new tasks by leveraging knowledge from prior tasks. However, previous studies often assume a single mode homogeneous task distribution, ignoring possible structured heterogeneity among tasks. Leveraging such structures can better facilitate knowledge sharing among related tasks and thus improve sample efficiency. In this paper, we explore the structured heterogeneity among tasks via clustering to improve meta-RL. We develop a dedicated exploratory policy to discover task structures via divide-and-conquer. The knowledge of the identified clusters helps to narrow the search space of task-specific information, leading to more sample efficient policy adaptation. Experiments on various MuJoCo tasks showed the proposed method can unravel cluster structures effectively in both rewards and state dynamics, proving strong advantages against a set of state-of-the-art baselines.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Meta-reinforcement learning (meta-RL) aims to quickly solve new tasks by leveraging knowledge from prior tasks. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | However, previous studies often assume a single mode homogeneous task distribution, ignoring possible structured heterogeneity among tasks. | gap 0.99 | 0.04 | 0.02 | asserted 0.90 |
| s2 | Leveraging such structures can better facilitate knowledge sharing among related tasks and thus improve sample efficiency. | background 0.81 | 0.07 | 0.08 | hedged possibility 0.75 |
| s3 | In this paper, we explore the structured heterogeneity among tasks via clustering to improve meta-RL. | contribution 0.99 | 0.77 | 0.08 | asserted 0.77 |
| s4 | We develop a dedicated exploratory policy to discover task structures via divide-and-conquer. | method 0.91 | 0.94 | 0.04 | asserted 1.00 |
| s5 | The knowledge of the identified clusters helps to narrow the search space of task-specific information, leading to more sample efficient policy adaptation. | interpretation 0.56 | 0.61 | 0.17 | asserted 0.96 |
| s6 | Experiments on various MuJoCo tasks showed the proposed method can unravel cluster structures effectively in both rewards and state dynamics, proving strong advantages against a set of state-of-the-art baselines. | result 1.00 | 0.97 | 0.82 | asserted 1.00 |
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The paper
Original abstract
Meta-reinforcement learning (meta-RL) aims to quickly solve new tasks by leveraging knowledge from prior tasks. However, previous studies often assume a single mode homogeneous task distribution, ignoring possible structured heterogeneity among tasks. Leveraging such structures can better facilitate knowledge sharing among related tasks and thus improve sample efficiency. In this paper, we explore the structured heterogeneity among tasks via clustering to improve meta-RL. We develop a dedicated exploratory policy to discover task structures via divide-and-conquer. The knowledge of the identified clusters helps to narrow the search space of task-specific information, leading to more sample efficient policy adaptation. Experiments on various MuJoCo tasks showed the proposed method can unravel cluster structures effectively in both rewards and state dynamics, proving strong advantages against a set of state-of-the-art baselines.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
What it cites · 3 sentences without a confident label
Uses the method or tool of 4 papers
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Specifically, we perform cluster-based posterior inference [ref] to infer the cluster of a new task according to its ongoing trajectory.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders · Introduction · 0.96
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We trained via Proximal Policy Optimization (PPO) [ref] and set the default cluster number $C$ to 4.
Proximal Policy Optimization Algorithms · Reward and State Dynamics Clustered Environments · 1.00
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Instead of optimizing the intractable objective in Eq.([ref]), we optimize its evidence lower bound (ELBO) w.r.t. the approximated posterior $q_θ(z, c|τ_:t)$ estimated via Monte Carlo sampling [ref] (full derivation can be found in Appendix [ref]), [equation] where $p_ω(z|c) = 𝒩(μ_ω(c), σ^2_ω(c))$ is a learnable cluster-specific prior, which is different from the simple Gaussian prior used in single-mode VAE [ref] .
Auto-Encoding Variational Bayes · Cluster-based Variational Inference with Consistency Regularization · 0.93
Compares with 1 paper
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We compared with several representative meta-RL baselines, including RL$^2$ [ref] , PEARL [ref] , VariBAD [ref] and MetaCURE [ref] .
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning · Reward and State Dynamics Clustered Environments · 0.99
Cites as background 2 papers
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Inspired by the learning process of humans, meta-reinforcement learning (meta-RL) is proposed to quickly learn new tasks by leveraging knowledge shared by related tasks [ref] .
Learning to reinforcement learn · Introduction · 0.86
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Inspired by the learning process of humans, meta-reinforcement learning (meta-RL) is proposed to quickly learn new tasks by leveraging knowledge shared by related tasks [ref] .
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning · Introduction · 0.86
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