Reinforcement Learning (RL)

Reinforcement Learning (RL) is distinct from supervised and unsupervised learning. Instead of learning from labeled data, an RL agent interacts with an environment and learns from the consequences of its actions.

Core RL concepts:

• Agent: The learner or decision-maker
• Environment: The world the agent interacts with
• State: The current situation
• Action: A move the agent can make
• Reward: Feedback signal (positive or negative)

Key achievements of RL include DeepMind’s AlphaGo (defeating world champion Go players), OpenAI Five (playing Dota 2), and training robots for complex manipulation tasks. RL is also central to RLHF (Reinforcement Learning from Human Feedback), the technique used to align LLMs with human preferences. Deep RL combines deep neural networks with RL algorithms to handle high-dimensional state spaces.