The course will provide students with advanced knowledge in deep reinforcement learning (RL) and covers topics such as deep Q-networks, policy gradients, stakeholder-critical models, ethical considerations and societal impacts. Students will develop practical skills in implementing deep reinforcement learning algorithms using neural networks, improving problem-solving capacity and mastering neural network optimisation. The course will also promote critical thinking, collaboration and ethical decision-making. Upon completion, students will be well-prepared for practical applications in artificial intelligence and able to make informed, ethical decisions and contribute to the advancement of the field.

• Deep reinforcement learning (RL)
  
  • deep Q-networks, policy gradients, stakeholder-critical methods, trade-offs for exploration-exploitation, multi-agent RL, model-based RL, transfer learning and meta-reinforcement learning
• Neural network architectures for RL
  
  • feedforward, convolutional and recurring networks with an understanding of network initialisation, regularisation techniques, batch normalisation and how to solve common problems such as overfitting, vanishing/exploding gradients and catastrophic forgetting