Projects
Multi-Agent RL Trading System
Reinforcement learning agents that learn to trade in simulated stock markets. Uses PPO to train multiple agents competing against each other, with realistic price impact modeling and portfolio management.
Results:
- • Agents learn stable trading strategies with consistent returns
- • Shows realistic market impact understanding through price correlations
- • Adapts behavior in real-time to market volatility
Reinforcement Learning
PPO Algorithm
Multi-Agent Systems
OpenAI Gym
View Project
Interactive LLM Explainability Dashboard
Interactive dashboard for exploring and visualizing how language models work internally. Makes complex neural network behaviors interpretable through attention heatmaps and text embedding visualizations.
Features:
- • Text embeddings visualization using SentenceTransformer with t-SNE
- • BERT attention heatmaps showing token-to-token attention patterns
- • Interactive interface for exploring model internals
Interpretability
Attention Visualization
BERT
t-SNE
View Project