Reinforcement Learning

Reinforcement learning (RL) is a machine learning paradigm where an intelligent agent learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties, and adjusting its strategy to maximize cumulative reward over time. Unlike supervised learning, which requires labeled examples, RL agents discover optimal behavior through exploration and experience, making it suited for sequential decision-making problems.

The RL framework consists of an agent that observes the current state of an environment, selects an action based on a policy, receives a reward signal, and transitions to a new state. The agent goal is to learn a policy that maximizes the expected sum of future rewards. Key algorithms include Q-learning and Deep Q-Networks (DQN) for discrete action spaces, policy gradient methods like PPO (Proximal Policy Optimization) for continuous actions, and actor-critic methods that combine value estimation with policy optimization.

Reinforcement learning from human feedback (RLHF) has become a critical technique in AI alignment, used to fine-tune large language models to be helpful, harmless, and honest. In RLHF, human annotators compare model outputs and express preferences, which train a reward model that is then used to optimize the language model policy. This technique, along with variants like Direct Preference Optimization (DPO), is responsible for the behavioral improvements that distinguish aligned chatbots from raw pre-trained models.

Beyond language models, RL powers applications in robotics control, game playing (AlphaGo, OpenAI Five), recommendation systems, resource allocation, autonomous vehicles, and industrial process optimization. Enterprise RL applications are growing in areas like dynamic pricing, supply chain optimization, and network routing. Key challenges include sample efficiency (RL typically requires many interactions to learn), reward design (poorly specified rewards lead to unintended behavior), safety during exploration, and the difficulty of deploying RL systems in environments where errors have real-world consequences.