Source code for Master's Thesis: Curiosity-driven Planning with Reinforcement Learning.
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Updated
Jun 23, 2023 - Jupyter Notebook
Source code for Master's Thesis: Curiosity-driven Planning with Reinforcement Learning.
Create and test your own cell colonies!
Pytorch Implementation of the World Models paper from 2018.
VQ-VAE-based image tokenizer for model-based RL
Master's thesis project on learning stateful simulations with deep differentiable models. The focus is to train a neural network to simulate a game (PONG) end-to-end.
A reinforcement learning project for crowd-dynamics in a very narrow corridor
Implementation of the paper <Model-based Reinforcement Learning for Predictions and Control for Limit Order Books (Wei et al., J.P. Morgan AI Research, 2019)>.
A new version of world models using Echo-state networks and random weight-fixed CNNs
Flax Implementation of DreamerV3 on Crafter
Toward Multi Modality Language Model - implementation of GPT-4o/Project Astra
VC-FB and MC-FB algorithms from "Zero-Shot Reinforcement Learning from Low Quality Data" (NeurIPS 2024)
Minimum viable reinforcement learning algorithms for your educational convenience.
[NeurIPS 2021] Contrastive learning formulation of the active inference framework, for matching visual goal states.
PyTorch World Model implementation with PPO.
I GAVE GPT-4 EYES!
Dreamer on JAX
Pytorch implementation of DreamerV2: Mastering Atari with Discrete World Models, based on the original implementation
We develop world models that can be adapted with natural language. Intergrating these models into artificial agents allows humans to effectively control these agents through verbal communication.
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