Q-Learner: Reinforcement Learning Trading
Reinforcement learning implementation using Q-learning and Dyna-Q for automated trading strategy development with experience replay and model-based planning.
Overview
A reinforcement learning project implementing Q-Learning for automated trading strategies. The project includes tabular Q-learning with epsilon-greedy exploration, Dyna-Q for model-based learning with experience replay, and application to real stock market data for trading decisions.
Technologies
Key Features
- ✓Tabular Q-Learning implementation
- ✓Epsilon-greedy exploration with decay
- ✓Dyna-Q model-based planning
- ✓Experience replay for sample efficiency
- ✓State discretization for continuous data
- ✓Trading action optimization (buy/sell/hold)
Challenges
Designing appropriate state representations for financial data, tuning exploration-exploitation tradeoff, implementing efficient Q-table updates, and handling the non-stationarity of financial markets.
Results
Successfully implemented a Q-Learner that develops profitable trading strategies through reinforcement learning, demonstrating understanding of RL principles in financial applications.