[ICLR 2025 Oral] PyTorch code for the paper "Open-World Reinforcement Learning over Long Short-Term Imagination"
A Simplified Pytorch Version of the Dreamer Algorithm
Recall to Imagine, a model-based RL algorithm with superhuman memory. Oral (1.2%) @ ICLR 2024
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From-scratch PyTorch implementation of DreamerV4 (Hafner et al., 2024): masked-autoencoder tokenizer, block-causal flow-matching dynamics with bootstrap curriculum, agent-token finetuning, and PMPO imagination RL. Hardened for TPU v4 / torch_xla with fixed-shape graphs, on-device RNG, and bounded compile-cache footprint.
Official implementation of the Informed Dreamer algorithm, based on DreamerV3
Simplistic Pytorch Implementation of the Dreamer-RL
A modular PyTorch library designed for learning, training, and deploying world models across various environments.
C++ Deep Reinforcement Learning Agent library
Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalization
[ICLR 2025 Oral] PyTorch code for the paper "Open-World Reinforcement Learning over Long Short-Term Imagination"
The implementation of pytorch-based DreamerV3 for Meta-world simulator.
Trains a deep reinforcement learning agent in simulation testbed environments with the DRLA library.
Curated papers, code, datasets, and benchmarks for medical world models in imaging, EHR trajectories, treatment planning, surgical AI, robotics, and virtual-cell simulation.
Train a neural world model on your own video data, then play it live with WASD. Record → Kaggle → Play
A Study on Reinforcement Learning in Starcraft Game Platform as a Collaborative Researcher of Samsung Company.
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