Dueling Double Deep Q-Networks for Regime-Aware and Risk-Conscious Stock Trading: Evidence from NIFTY 50

Reinforcement learning (RL) has emerged as a promising approach for automated stock trading, but standard deep Q-networks (DQNs) often struggle with volatile market dynamics and risk management. This paper presents a comprehensive study of trading the NIFTY 50 index using an RL agent based on DQN, enhanced with multiple novel improvements. We propose an adaptive action space that flexibly adjusts to market volatility, market regime detection via unsupervised learning (e.g., Hidden Markov Models or K-Means clustering) to inform the agent of bull/bear phases, and a composite reward function that balances profit with a Sharpe ratio proxy, drawdown penalties, and transaction costs. The DQN algorithm is further refined through risk-aware Q-value updates using soft Q-learning principles to encourage prudent exploration, and a multi-objective dueling network architecture that separately estimates profit and drawdown-centric value functions. To improve generalization, the agent is trained with synthetic data augmentation using noise injection and generative modeling, and its state representation is compressed via an autoencoder bottleneck to denoise and reduce dimensionality. The trading environment includes budget constraints and leverage mechanics to simulate margin trading, and incorporates macroeconomic indicators (USD/INR exchange rate, inflation, oil prices) into the state space for a richer market context. Using a modified Chainer DQN framework, we evaluate variants of DQN, Double DQN, and Dueling Double DQN on historical NIFTY 50 data. The results show that our improved Dueling Double DQN agent achieves superior performance, with higher profitability and risk-adjusted returns (Sharpe proxy) on the test period, compared to baseline approaches. These findings illustrate how integrating domain-specific insights (volatility, regimes, macro factors) and advanced RL techniques can yield a robust trading agent for emerging market indices like NIFTY 50.

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