Course Structure

Part 0: Fundamentals of Reinforcement Learning

Q-learning Intuition

Q-learning is a model-free reinforcement learning algorithm that aims to learn the value of an action in a particular state. It uses a Q-table to store Q-values, which represent the expected rewards of taking certain actions in certain states. The goal is to update this table iteratively to converge on an optimal policy.

Q-learning Visualization

Visualizing the Q-learning process can help understand how agents explore the environment and update their Q-values. Typical visualizations include the agent's movement through states, reward accumulation, and changes in the Q-table over time.

Part 1: Deep Q Learning

Deep Q Learning Intuition

Deep Q Learning (DQN) extends Q-learning by using deep neural networks to approximate the Q-function. This is useful in environments with large state spaces where creating a Q-table is infeasible. DQNs combine reinforcement learning with deep learning, allowing the agent to take actions based on visual input or other complex data.

Deep Q Learning Implementation

Implementing DQNs involves creating a neural network that outputs Q-values for all possible actions, given a state as input. Key elements include experience replay and target networks to stabilize the learning process.

Part 2: Deep Convolution Q-Learning

Deep Convolution Q-Learning Intuition

Deep Convolutional Q-Learning incorporates convolutional neural networks (CNNs) to handle high-dimensional input spaces, such as images. This approach allows the agent to learn directly from raw pixel data, improving its ability to make decisions in visual tasks.

Deep Convolution Q-Learning Implementation

The implementation involves designing a CNN to extract spatial features from input images, which are then fed into a Q-network for action-value estimation. Special techniques like frame stacking and downsampling help improve performance.

Part 3: A3C (Asynchronous Advantage Actor-Critic)

A3C Intuition

A3C is a reinforcement learning algorithm that operates multiple agents in parallel environments, allowing them to learn asynchronously. It uses two neural networks: an actor to choose actions and a critic to evaluate the action. The advantage function helps reduce variance in policy gradient estimates.

A3C Implementation

To implement A3C, we need to create both actor and critic networks and set up multiple environments running in parallel. As the agents interact with their environments, they update the shared global network asynchronously.

Part 4: PPO and SAC

PPO and SAC Intuition

Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) are two popular policy optimization algorithms. PPO is known for its simplicity and effectiveness, using a clipped objective function to ensure stable updates. SAC is an off-policy method that maximizes the entropy of the policy, encouraging exploration.

PPO and SAC Implementation

PPO involves creating a policy network and applying clipping to the policy updates, while SAC incorporates an entropy term into the objective to balance exploration and exploitation. Both methods require maintaining separate networks for policy and value functions.

Part 5: Introduction to Large Language Models (LLMs)

LLMs Intuition

Large Language Models (LLMs) like GPT-3 and BERT are neural networks trained on massive amounts of text data. They learn to predict the next word in a sentence, allowing them to generate human-like text. Their power lies in their ability to generalize across various natural language tasks.

LLMs Implementation

Implementing LLMs involves fine-tuning pre-trained models on specific tasks or training a new model using large datasets and powerful compute resources. Techniques like transfer learning and tokenization play a crucial role in building these models.