Memory-enhanced reinforcement learning architecture
Explore multimodal tasks powered by hybrid GPT-4 and innovative memory modules for enhanced accuracy.
Dataset
Multimodal tasks requiring long-term memory (e.g., medical diagnosis simulations).
Architecture
Hybrid GPT-4 + memory module (Transformer-based key-value store).
Experiments
Compare with GPT-3.5 and non-memory RL baselines.
API Usage
Dynamic fine-tuning with custom memory-enhanced loss functions.
Technical
Demonstrate GPT-4’s superiority in memory-augmented RL.
Societal
Reduce biases in high-stakes decisions (e.g., healthcare).
OpenAI
Insights into memory capacity expansion via fine-tuning

