# opendilab/awesome-model-based-RL

A curated list of awesome model based RL resources (continually updated)

Repository: https://github.com/opendilab/awesome-model-based-RL
Canonical: https://ross.abutalabs.com/products/awesome-model-based-rl
License: Apache-2.0
License Family: permissive
Topics: reinforcement-learning, reinforcement-learning-algorithms, model-based-reinforcement-learning, model-based-rl, awesome, awesome-list
Last push: 2026-05-21T07:55:38+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 83, release rhythm 35, longevity 100
- inputs: {"age_days": 1709, "days_push": 104, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1393, forks 79 (observed 2026-08-28T04:04:36.190912+00:00)

## What it is
A curated, continually updated list of research papers, tutorials, and codebases for model-based reinforcement learning (MBRL). It organizes papers by venue and year, from classic works through ICML/ICLR 2026, and includes a taxonomy of MBRL algorithms.

## Use cases
- find model-based reinforcement learning papers
- survey the state of the art in MBRL
- learn about model-based RL algorithms and taxonomy
- track new MBRL papers from NeurIPS, ICML, and ICLR
- find codebases for model-based RL research
- get started learning model-based reinforcement learning

## When to choose
- you need a curated, up-to-date reading list for model-based RL research
- you want papers organized by conference and year with a taxonomy overview
- you are surveying the MBRL literature for a project or literature review

## When to avoid
- you need runnable RL algorithms or a training framework rather than a paper list
- you are looking for model-free reinforcement learning resources
- you need tutorials for beginners rather than research papers

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: reinforcement-learning, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, model-based-rl, research-papers, curated-resources, reinforcement-learning

## Member repositories
- opendilab/awesome-model-based-RL (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.190912+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:30.025605+00:00, confidence not recorded.
  - readme: https://github.com/opendilab/awesome-model-based-RL (fetched 2026-08-28T04:04:36.190912+00:00, sha 6ea13fc0b761)
- Data as of 2026-08-30T08:39:29.467469+00:00.