[ICLR 2025 Oral] PyTorch code for the paper "Open-World Reinforcement Learning over Long Short-Term Imagination"
Modular DRL framework for autonomous robot navigation in ROS2. Plug-and-play RL backends (Stable-Baselines3, DreamerV3), composable reward functions, observation spaces & neural architectures - built for research and deployment.
Flax Implementation of DreamerV3 on Crafter
PyTorch implementation of DreamerV3 from "Mastering Diverse Domains with World Models"
Learning to fly FPV but in dreams!
DreamerX is an implementation of model-based DreamerV3 with minor optimizations and novel training adjustments. It is designed to be flexible and user-friendly, allowing researchers and practitioners to easily interchange components and environments.
DreamerV3 World Model RL from Scratch — Educational implementation of model-based reinforcement learning
Hierarchical learning by dreaming for empowering control with latent skills in imagination
The implementation of pytorch-based DreamerV3 for Meta-world simulator.
The World Model Remembers, the Actor Forgets: Dream Rehearsal for Continual Model-Based RL — code, pre-registration trail, and run data
[ICLR 2025 Oral] PyTorch code for the paper "Open-World Reinforcement Learning over Long Short-Term Imagination"
Hardened FH6 self-driving research stack: screen capture, Forza Data Out telemetry, DreamerV3, AutoDrive recovery demos, and route-aware stress-tested training.
A template for deploying DreamerV3 with Ray RLlib, compatible with Gym and custom environments.
Experimental Model-Based RL agent for Pokémon Red. Implements continuous (VAE+GRU) and discrete Recurrent State-Space Models (RSSM) to learn environment dynamics. Features ablation studies on latent imagination drift, scheduled sampling, and MPC planning, inspired by Dreamer and DIAMOND architectures.
Uncertainty-gated imagination learning and failure-safe training for DreamerV3.
🌍 Investigating the understanding of spatio-temporal information in World Models | Research Project in World Models 2024 by The University of Tokyo
Reinforcement Learning : Autonomous parallel parking task. implementing SAC and DreamerV3's World Model on Highway-env
Extending DreamerV3 for robotic application and better Morphologieawareness by implementing a FFKSM
Oneiro — mini-DreamerV3 world model (15M params, JAX/Flax NNX) on the Crafter benchmark. Full debugging journals included.
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