This module explores practical strategies to accelerate reinforcement learning (RL) training, focusing on deep Q-network (DQN) improvements. Learners will investigate performance bottlenecks, experiment with batch sizes and parallelization, and understand the impact of environment wrappers on training efficiency. By the end, you'll be equipped to optimize RL workflows for faster convergence.

This module guides learners through applying deep Q-network (DQN) reinforcement learning techniques to real-world stock trading scenarios. You will work with historical Russian stock market data and explore different DQN architectures, including feed-forward and convolutional models, to develop and evaluate trading strategies.

This module introduces policy gradient methods as an alternative approach to solving Markov decision process problems in reinforcement learning. Learners will explore the mathematical foundations, implementation details, and practical considerations such as gradient variance and hyperparameter tuning. By working through real-world examples like CartPole, students will gain hands-on experience optimizing policies using neural networks.

This module introduces policy-based reinforcement learning through actor-critic methods, focusing on A2C and A3C algorithms. Learners will explore how these methods reduce variance in policy gradients, implement parallel environments, and apply these techniques to classic control and Atari games. Practical coding exercises and performance analysis are included to solidify understanding.

This module introduces learners to solving text-based interactive fiction games using reinforcement learning within the TextWorld environment. You will explore game generation, deep NLP fundamentals, word embeddings, and preprocessing pipelines, culminating in training agents and integrating large language models like ChatGPT for automated gameplay. By the end, you'll understand how to process complex textual observations and apply RL techniques to dynamic, language-rich environments.

This module explores how reinforcement learning can be applied to web navigation and browser automation tasks. Learners will experiment with simple RL agents in the MiniWoB environment, address challenges unique to browser automation, and enhance agent performance using text descriptions and human demonstrations.