MS
M.R. Serban
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Effects of exploration-exploitation strategies in dynamic Forex markets
The use of Reinforcement Learning in Algorithmic Trading
Bachelor thesis
(2025)
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M.R. Serban, M.A. Sharifi Kolarijani, A. Papapantoleon, N. Yorke-Smith, I.M. Olkhovskaia
This paper examines how different exploration strategies affect the learning behavior and trading performance of reinforcement learning (RL) agents in a custom foreign exchange (forex) environment. By holding all other components constant—including model architecture, features, and reward function—the study isolates the role of exploration in deep Q-learning. Three strategies were compared: Epsilon-Greedy, Boltzmann, and Max-Boltzmann. The hybrid Max-Boltzmann approach delivered the most stable and profitable outcomes, suggesting that weighted, value-aware exploration can be beneficial in highrisk domains. The results also highlight the impact of non-Markovian structure in financial environments and limitations of equity-based rewards. Beyond empirical results, this work contributes a modular, reproducible RL framework for trading, and opens new questions about the suitability of exploration techniques in environments with asymmetric risk and irreversible actions.
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This paper examines how different exploration strategies affect the learning behavior and trading performance of reinforcement learning (RL) agents in a custom foreign exchange (forex) environment. By holding all other components constant—including model architecture, features, and reward function—the study isolates the role of exploration in deep Q-learning. Three strategies were compared: Epsilon-Greedy, Boltzmann, and Max-Boltzmann. The hybrid Max-Boltzmann approach delivered the most stable and profitable outcomes, suggesting that weighted, value-aware exploration can be beneficial in highrisk domains. The results also highlight the impact of non-Markovian structure in financial environments and limitations of equity-based rewards. Beyond empirical results, this work contributes a modular, reproducible RL framework for trading, and opens new questions about the suitability of exploration techniques in environments with asymmetric risk and irreversible actions.