SoftwareApplication
name: trtpi
description: Code underlying the publication: Trust-Region Twisted Policy Improvement
description: TRT-SMC: Trust-Region Twisted Sequential Monte-Carlo — JAX implementation accompanying the ICML 2025 paper "Trust-Region Twisted Policy Improvement". Combines MCTS-inspired design choices (trust-region constrained action sampling, explicit terminal handling, improved policy/value targets) with particle filter planning to construct a GPU-efficient SMC policy improvement operator. Outperforms baseline MCTS and SMC planners in runtime and sample-efficiency across discrete and continuous RL domains. The smz module exposes a modular SMC planner accepting custom Proposal, Transition, and Target implementations, usable as a drop-in policy replacement compatible with jax.jit and jax.vmap.
code repository: https://data.4tu.nl/v3/datasets/ea1ffb14-6570-4ab1-9cf1-29f1e92186ec.git
author: https://orcid.org/0009-0008-2927-7448
author: https://orcid.org/0000-0002-8528-8650
author: Person
  given name: Yaniv
  family name: Oren
  name: Yaniv Oren
author: Person
  given name: Matthijs
  family name: Spaan
  name: Matthijs Spaan
keywords: Markov decision process
keywords: Autonomous Path-Planning
keywords: model-predictive control
keywords: Reinforcement learning
keywords: deep learning
programming language: Jupyter Notebook
programming language: Markdown
programming language: Other
programming language: Python
programming language: Shell
programming language: YAML
license:
  creative work: creativeWork
    name: MIT
reference publication: arxiv.2504.06048

https://orcid.org/0009-0008-2927-7448
type: Person
given name: Joery
family name: de Vries
name: Joery de Vries

https://orcid.org/0000-0002-8528-8650
type: Person
given name: Jinke
family name: He
name: Jinke He

arxiv.2504.06048
type: ScholarlyArticle
name: Trust-Region Twisted Policy Improvement
url: https://doi.org/10.48550%2Farxiv.2504.06048