This study explores how different features impact a Reinforcement Learning agent's performance in forex trading. Using a Deep Q-Network (DQN) agent and EUR/USD data from 2022-2024, we found that performance is highly sensitive to the information provided. Key findings show that for feature types like momentum and volatility, a single indicator outperformed a combination of them, as the latter tended to introduce noise. Including information about the agent's own status, such as its current trade duration, was beneficial. Counter-intuitively, providing more historical data consistently worsened performance, leading to overfitting where the agent memorized training data rather than learning general strategies. The main conclusion is that creating an effective state representation is a trade-off; the complexity of the input data must match the learning algorithm's ability to process it without overfitting.

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.

Algorithmic trading already dominates modern financial markets, yet most live systems still rely on fixed heuristics that falter when conditions change. Deep reinforcement learning agents promise adaptive decision making, but their behaviour is driven entirely by the reward function - a design choice that remains poorly researched. Given the critical importance of reward function design in RL problems, this paper investigates the impact of different choices of reward functions while trading in the Forex market and focusing on the EUR/USD pair. We explore different rewarding methods such as profit‑only, risk‑adjusted, multi‑objective, imitation‑learning based, and self‑rewarding mechanisms. Overall, we demonstrate the impact of careful reward engineering and whether it can boost performance and efficiency, highlighting reward design as a critical and previously under‑examined part of RL for deploying reliable trading models.

This paper investigates the effectiveness of transfer learning techniques for accelerating the training of deep reinforcement learning (RL) agents in the foreign exchange (Forex) market. Specifically, the transfer of policies learned on the EUR/USD currency pair to the GBP/USD pair is the focus. Four transfer learning approaches are systematically compared: zero-shot transfer, full fine-tuning, partial fine-tuning, and reward-function transfer. A modular pipeline was developed, incorporating sinusoidal and trend/momentum-based market features, stepwise agent-specific metrics, and deep Q-network (DQN) architectures from the Stable Baselines 3 framework. Agent performance is evaluated through cumulative reward and Sharpe ratio metrics.
Experimental results demonstrate that partial fine-tuning accelerates initial learning by preserving generic market features acquired from the EUR/USD pair. However, our results indicate that directly training on the target currency pair yields superior ultimate performance, highlighting the nuanced limitations and potential benefits of cross-currency transfer learning in algorithmic trading.

As a component of lithography machine, the wafer table plays an important role during wafer
exposure. The surface flatness condition of the WT will cause focus and overlay errors during
exposure and directly affect wafer distortion levels. This project takes advantage of global genetic algorithm to successfully develop a routing optimization design tool in MATLAB to deliver an efficient routing for genetic wafer fingerprints.

Market making, the act of providing liquidity to the market by simultaneously buying and selling, is a difficult problem to solve. The use of reinforcement learning to solve for market making is increasing, as academics and practitioners alike look for novel ways to approximate for optimal policies in increasingly complex markets. This thesis examines the use of reinforcement learning to solve the market making problem in limit order books. To this end, we provide the theoretical background on market making, modelling limit order books, and reinforcement learning. Furthermore, we implement and compare ’classical’ algorithms, such as Q-learning and value and policy iteration, and compare their policies with newer techniques involving deep learning, namely deep Q-networks and double deep Q-networks. To train and compare these models, we use two models to simulate the dynamics of an order book. Experiment results and the ultimately learned policies are presented and discussed. We propose several ideas that could be worked on in the future.

This thesis introduces a new method, called Mixed Iteration, for controlling Markov Decision Processes when partial information is known about the dynamics of the Markov Decision Process. The algorithm uses sampling to calculate the expectation of partially known dynamics in stochastic environments. Its goal is to lower the number of iterations and computational steps required for convergence compared to traditional model-free algorithms. By lowering the number of samples required to achieve convergence Markov Decision Processes can be controlled and trained more efficiently. Additionally, the thesis discusses how this algorithm can enhance the sample efficiency and convergence rate of Reinforcement Learning algorithms like Q-Learning. The effectiveness of the proposed method will be evaluated in standard Reinforcement Learning problems and compared with the performance of Q-learning. The results show that under certain conditions that will be discussed in the thesis, the new proposed algorithm outperforms classical algorithms in terms of sample efficiency. The study will provide insight into the field of previous partial information in Reinforcement Learning alternatives, as well as the challenges that researchers in this field continue to face.