Reinforcement Learning
Reinforcement Learning (RL) is a paradigm in artificial intelligence where an agent learns to make decisions through interaction with an environment. At its core, RL is about trial and error: the agent takes an action, receives feedback in the form of a reward or penalty, and updates its policy to maximize long-term cumulative reward.
This approach has several key components:
- Agent – the decision-making entity.
- Environment – the system with which the agent interacts.
- Reward function – the signal that guides learning.
- Policy – the mapping from states to actions.
- Value function – an estimation of expected future rewards.
RL has been central to some of the most iconic AI achievements:
- Game playing: Atari games, Chess, Go (AlphaGo).
- Robotics: training autonomous robots for locomotion and manipulation.
- Operations research: supply chain optimization, traffic management.
- Healthcare: treatment policy optimization and adaptive therapies.
Yet RL is not without limitations. Training an agent often requires enormous amounts of data and computing power. The reward design problem is also central: if rewards are misspecified, agents can exploit loopholes or develop unintended strategies (“reward hacking”). Moreover, real-world environments are often non-stationary and noisy, which complicates learning stability.
Research continues to expand into Deep Reinforcement Learning, where neural networks approximate policies and value functions, opening doors to complex, high-dimensional tasks.
🔗 References:
- Sutton, R. & Barto, A. (2020). Reinforcement Learning: An Introduction. Free PDF.
- OpenAI – Spinning Up in Learning by RLHF for LLMs and other models
- Innovatiana - Learning by RLHF for LLMs and other models
