Author name cluster

Risto Vuorio

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11 papers

2 author rows

ICML Conference 2025 Conference Paper

Action-Constrained Imitation Learning

Chia-Han Yeh
Tse-Sheng Nan
Risto Vuorio
Wei Hung
Hung-Yen Wu
Shao-Hua Sun
Ping-Chun Hsieh

Policy learning under action constraints plays a central role in ensuring safe behaviors in various robot control and resource allocation applications. In this paper, we study a new problem setting termed Action-Constrained Imitation Learning (ACIL), where an action-constrained imitator aims to learn from a demonstrative expert with larger action space. The fundamental challenge of ACIL lies in the unavoidable mismatch of occupancy measure between the expert and the imitator caused by the action constraints. We tackle this mismatch through trajectory alignment and propose DTWIL, which replaces the original expert demonstrations with a surrogate dataset that follows similar state trajectories while adhering to the action constraints. Specifically, we recast trajectory alignment as a planning problem and solve it via Model Predictive Control, which aligns the surrogate trajectories with the expert trajectories based on the Dynamic Time Warping (DTW) distance. Through extensive experiments, we demonstrate that learning from the dataset generated by DTWIL significantly enhances performance across multiple robot control tasks and outperforms various benchmark imitation learning algorithms in terms of sample efficiency.

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IROS Conference 2025 Conference Paper

IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving

Clémence Grislain
Risto Vuorio
Cong Lu
Shimon Whiteson

Developing autonomous vehicles that can navigate complex environments with human-level safety and efficiency is a central goal in self-driving research. A common approach to achieving this is imitation learning, where agents are trained to mimic human expert demonstrations collected from real- world driving scenarios. However, discrepancies between human perception and the self-driving car's sensors can introduce an imitation gap, leading to imitation learning failures. In this work, we introduce IGDrivSim, a benchmark built on top of the Waymax simulator, designed to investigate the effects of the imitation gap in learning autonomous driving policy from human expert demonstrations. Our experiments show that this perception gap between human experts and selfdriving agents can hinder the learning of safe and effective driving behaviors. We further show that combining imitation with reinforcement learning, using a simple penalty reward for prohibited behaviors, effectively mitigates these failures. All code developed for this work is released as open source 1.

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TMLR Journal 2024 Journal Article

Deconfounding Imitation Learning with Variational Inference

Risto Vuorio
Pim De Haan
Johann Brehmer
Hanno Ackermann
Daniel Dijkman
Taco Cohen

Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent. This is because partial observability gives rise to hidden confounders in the causal graph. In previous work, to work around the confounding problem, policies have been trained using query access to the expert’s policy or inverse reinforcement learning (IRL). However, both approaches have drawbacks as the expert’s policy may not be available and IRL can be unstable in practice. Instead, we propose to train a variational inference model to infer the expert’s latent information and use it to train a latent-conditional policy. We prove that using this method, under strong assumptions, the identification of the correct imitation learning policy is theoretically possible from expert demonstrations alone. In practice, we focus on a setting with less strong assumptions where we use exploration data for learning the inference model. We show in theory and practice that this algorithm converges to the correct interventional policy, solves the confounding issue, and can under certain assumptions achieve an asymptotically optimal imitation performance.

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ICML Conference 2024 Conference Paper

Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control

Zheng Xiong
Risto Vuorio
Jacob Beck
Matthieu Zimmer
Kun Shao
Shimon Whiteson

Learning a universal policy across different robot morphologies can significantly improve learning efficiency and enable zero-shot generalization to unseen morphologies. However, learning a highly performant universal policy requires sophisticated architectures like transformers (TF) that have larger memory and computational cost than simpler multi-layer perceptrons (MLP). To achieve both good performance like TF and high efficiency like MLP at inference time, we propose HyperDistill, which consists of: (1) A morphology-conditioned hypernetwork (HN) that generates robot-wise MLP policies, and (2) A policy distillation approach that is essential for successful training. We show that on UNIMAL, a benchmark with hundreds of diverse morphologies, HyperDistill performs as well as a universal TF teacher policy on both training and unseen test robots, but reduces model size by 6-14 times, and computational cost by 67-160 times in different environments. Our analysis attributes the efficiency advantage of HyperDistill at inference time to knowledge decoupling, i. e. , the ability to decouple inter-task and intra-task knowledge, a general principle that could also be applied to improve inference efficiency in other domains. The code is publicly available at https: //github. com/MasterXiong/Universal-Morphology-Control.

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RLC Conference 2024 Conference Paper

SplAgger: Split Aggregation for Meta-Reinforcement Learning

Jacob Beck
Matthew Thomas Jackson
Risto Vuorio
Zheng Xiong
Shimon Whiteson

A core ambition of reinforcement learning (RL) is the creation of agents capable of rapid learning in novel tasks. Meta-RL aims to achieve this by directly learning such agents. Black box methods do so by training off-the-shelf sequence models end-to-end. By contrast, task inference methods explicitly infer a posterior distribution over the unknown task, typically using distinct objectives and sequence models designed to enable task inference. Recent work has shown that task inference methods are not necessary for strong performance. However, it remains unclear whether task inference sequence models are beneficial even when task inference objectives are not. In this paper, we present evidence that task inference sequence models are indeed still beneficial. In particular, we investigate sequence models with permutation invariant aggregation, which exploit the fact that, due to the Markov property, the task posterior does not depend on the order of data. We empirically confirm the advantage of permutation invariant sequence models without the use of task inference objectives. However, we also find, surprisingly, that there are multiple conditions under which permutation variance remains useful. Therefore, we propose SplAgger, which uses both permutation variant and invariant components to achieve the best of both worlds, outperforming all baselines evaluated on continuous control and memory environments. Code is provided at https: //github. com/jacooba/hyper.

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RLJ Journal 2024 Journal Article

SplAgger: Split Aggregation for Meta-Reinforcement Learning

Jacob Beck
Matthew Thomas Jackson
Risto Vuorio
Zheng Xiong
Shimon Whiteson

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NeurIPS Conference 2023 Conference Paper

Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design

Matthew T Jackson
Minqi Jiang
Jack Parker-Holder
Risto Vuorio
Chris Lu
Greg Farquhar
Shimon Whiteson
Jakob Foerster

The past decade has seen vast progress in deep reinforcement learning (RL) on the back of algorithms manually designed by human researchers. Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks. Despite impressive initial results from algorithms such as Learned Policy Gradient (LPG), there remains a generalization gap when these algorithms are applied to unseen environments. In this work, we examine how characteristics of the meta-training distribution impact the generalization performance of these algorithms. Motivated by this analysis and building on ideas from Unsupervised Environment Design (UED), we propose a novel approach for automatically generating curricula to maximize the regret of a meta-learned optimizer, in addition to a novel approximation of regret, which we name algorithmic regret (AR). The result is our method, General RL Optimizers Obtained Via Environment Design (GROOVE). In a series of experiments, we show that GROOVE achieves superior generalization to LPG, and evaluate AR against baseline metrics from UED, identifying it as a critical component of environment design in this setting. We believe this approach is a step towards the discovery of truly general RL algorithms, capable of solving a wide range of real-world environments.

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NeurIPS Conference 2023 Conference Paper

Recurrent Hypernetworks are Surprisingly Strong in Meta-RL

Jacob Beck
Risto Vuorio
Zheng Xiong
Shimon Whiteson

Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample inefficiency. Meta-RL directly addresses this sample inefficiency by learning to perform few-shot learning when a distribution of related tasks is available for meta-training. While many specialized meta-RL methods have been proposed, recent work suggests that end-to-end learning in conjunction with an off-the-shelf sequential model, such as a recurrent network, is a surprisingly strong baseline. However, such claims have been controversial due to limited supporting evidence, particularly in the face of prior work establishing precisely the opposite. In this paper, we conduct an empirical investigation. While we likewise find that a recurrent network can achieve strong performance, we demonstrate that the use of hypernetworks is crucial to maximizing their potential. Surprisingly, when combined with hypernetworks, the recurrent baselines that are far simpler than existing specialized methods actually achieve the strongest performance of all methods evaluated. We provide code at https: //github. com/jacooba/hyper.

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AAAI Conference 2022 Conference Paper

Adaptive Pairwise Weights for Temporal Credit Assignment

Zeyu Zheng
Risto Vuorio
Richard Lewis
Satinder Singh

How much credit (or blame) should an action taken in a state get for a future reward? This is the fundamental temporal credit assignment problem in Reinforcement Learning (RL). One of the earliest and still most widely used heuristics is to assign this credit based on a scalar coefficient, lambda (treated as a hyperparameter), raised to the power of the time interval between the state-action and the reward. In this empirical paper, we explore heuristics based on more general pairwise weightings that are functions of the state in which the action was taken, the state at the time of the reward, as well as the time interval between the two. Of course it isn’t clear what these pairwise weight functions should be, and because they are too complex to be treated as hyperparameters we develop a metagradient procedure for learning these weight functions during the usual RL training of a policy. Our empirical work shows that it is often possible to learn these pairwise weight functions during learning of the policy to achieve better performance than competing approaches.

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NeurIPS Conference 2021 Conference Paper

Learning State Representations from Random Deep Action-conditional Predictions

Zeyu Zheng
Vivek Veeriah
Risto Vuorio
Richard L Lewis
Satinder Singh

Our main contribution in this work is an empirical finding that random General Value Functions (GVFs), i. e. , deep action-conditional predictions---random both in what feature of observations they predict as well as in the sequence of actions the predictions are conditioned upon---form good auxiliary tasks for reinforcement learning (RL) problems. In particular, we show that random deep action-conditional predictions when used as auxiliary tasks yield state representations that produce control performance competitive with state-of-the-art hand-crafted auxiliary tasks like value prediction, pixel control, and CURL in both Atari and DeepMind Lab tasks. In another set of experiments we stop the gradients from the RL part of the network to the state representation learning part of the network and show, perhaps surprisingly, that the auxiliary tasks alone are sufficient to learn state representations good enough to outperform an end-to-end trained actor-critic baseline. We opensourced our code at https: //github. com/Hwhitetooth/random_gvfs.

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NeurIPS Conference 2019 Conference Paper

Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation

Risto Vuorio
Shao-Hua Sun
Hexiang Hu
Joseph Lim

Model-agnostic meta-learners aim to acquire meta-learned parameters from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. With the flexibility in the choice of models, those frameworks demonstrate appealing performance on a variety of domains such as few-shot image classification and reinforcement learning. However, one important limitation of such frameworks is that they seek a common initialization shared across the entire task distribution, substantially limiting the diversity of the task distributions that they are able to learn from. In this paper, we augment MAML with the capability to identify the mode of tasks sampled from a multimodal task distribution and adapt quickly through gradient updates. Specifically, we propose a multimodal MAML (MMAML) framework, which is able to modulate its meta-learned prior parameters according to the identified mode, allowing more efficient fast adaptation. We evaluate the proposed model on a diverse set of few-shot learning tasks, including regression, image classification, and reinforcement learning. The results not only demonstrate the effectiveness of our model in modulating the meta-learned prior in response to the characteristics of tasks but also show that training on a multimodal distribution can produce an improvement over unimodal training. The code for this project is publicly available at https: //vuoristo. github. io/MMAML.

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