- Implementing Deep Reinforcement Learning Algorithms (k3)
Students are able to implement DRL algorithms such as Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic approaches in practical applications.
- Training and Optimizing DRL Agents (k5)
Students can train DRL agents using techniques like experience replay, reward shaping, and exploration strategies to improve learning efficiency and stability.
- Applying DRL in Real-World Scenarios (k5)
Students are able to apply DRL models to complex environments, such as robotics, autonomous driving, and strategic game playing, ensuring robust decision-making under uncertainty.
- Evaluating DRL Model Performance (k5)
Students can assess the effectiveness of DRL models by analyzing learning curves, reward signals, and policy stability, adjusting hyperparameters for optimal performance.
- Understanding the Challenges and Limitations of DRL (k5)
Students are capable of identifying the challenges of DRL, such as sample inefficiency, reward sparsity, and exploration-exploitation trade-offs, proposing solutions to mitigate these issues.
|
Students possess knowledge of deep reinforcement learning principles, covering key techniques such as Deep Q-Learning, Policy Gradients, and Actor-Critic methods. They know how to apply DRL to various domains, optimizing agent behavior in dynamic and uncertain environments.
|
