Teaching the Donkey car to drive a track in the simulator using State Representation Learning and different Reinforcement Learning Algorithms including Deep Q-Network, Soft Actor-Critic and Proximal Policy Optimization Algorithms.

Building an LLM with RLHF involves fine-tuning using human-labeled preferences. Based on Learning to Summarize from Human Feedback, it uses supervised learning, reward modeling, and PPO to improve response quality and alignment.