Clean baseline implementation of PPO using an episodic TransformerXL memory
A minimalist MVP demonstrating a simple yet profound insight: aligning AI memory with human episodic memory granularity. Shows how this single principle enables simple methods to rival complex memory frameworks for conversational tasks.
Code for paper "Episodic Memory Deep Q-Networks" (https://arxiv.org/abs/1805.07603), IJCAI 2018
Lu, Q., Hasson, U., & Norman, K. A. (2022). A neural network model of when to retrieve and encode episodic memories. eLife
A Python(PyTorch) implementation of memory augmented neural network based on Ritter et al. (2018). Been There, Done That: Meta-Learning with Episodic Recall. ICML.
PyTorch implementation of Episodic Meta Reinforcement Learning on variants of the "Two-Step" task. Reproduces the results found in three papers. Check the ReadMe for more details!
NaQ: Leveraging Narrations as Queries to Supervise Episodic Memory. CVPR 2023.
Psifr: Analysis and visualization of free recall data
A machine with human-like memory systems.
A PyTorch library that replaces standard replay buffers with a learnable memory system.
Continual Learning methods using Episodic Memory (CLEM) in PyTorch
Memory-centric architecture for scalable, energy-efficient AI agents and long-term cognition.
The OpenAI-Gym-compatible Room environment.
Code for exploring how we distribute our thoughts over time when we remember, using data from a naturalistic memory experiment.
Stop your AI agent from repeating same mistake and learn from previous failures
Living Memory Dynamics - A novel framework for narrative-generating episodic memory with creative leaps. Features the Joshua R. Thomas Memory Equation.
A research-oriented Python framework implementing the DREAM architecture for long-term episodic memory in AI agents.
Build an AI Agent with long term memory using LangGraph
The content of this repository will be inherent to the Computational Intelligence course at Polytechnic University of Turin academic year 2023/2024
Official implementation of the paper "Linking In-context Learning in Transformers to Human Episodic Memory" by Li Ji-An, Corey Zhou, Marcus Benna, and Marcelo Mattar
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