This course covers sophisticated reinforcement learning algorithms and methodologies. It focuses on deep reinforcement learning, policy optimization, exploration strategies, model-based methods, and multi-agent systems. Gain theoretical understanding and practical implementation skills for complex sequential decision-making problems.
Prerequisites Python, ML & RL Fundamentals
Level:
Deep Q-Network Variants
Implement and understand algorithms like DQN, Double DQN, Dueling DQN, and Prioritized Experience Replay.
Advanced Policy Gradient Methods
Grasp the theory and application of A2C, A3C, DDPG, TRPO, and PPO.
Sophisticated Exploration Strategies
Apply advanced exploration techniques beyond epsilon-greedy, including curiosity-driven and count-based methods.
Model-Based Reinforcement Learning
Develop agents that learn environment models and use them for planning (e.g., Dyna-Q, MCTS integration concepts).
Multi-Agent Reinforcement Learning
Understand the challenges and implement algorithms for cooperative and competitive multi-agent scenarios (e.g., MADDPG).
Offline Reinforcement Learning
Learn techniques to train RL agents from fixed datasets, addressing challenges like distributional shift.
Implementation and Optimization
Optimize deep RL implementations, tune hyperparameters, and debug agent behavior effectively.
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