Reinforcement learning (RL) is a field of artificial intelligence in which an agent learns to make sequential decisions by interacting with an environment. The agent receives rewards or penalties based on its actions and adapts its strategy (or policy) to maximize cumulative long-term reward. Unlike supervised learning, where correct answers are provided, RL is distinguished by the agent having to discover which sequence of actions leads to success, often through trial and error.
Use Cases and Examples
Reinforcement learning is used in robotics (for learning to manipulate objects or navigate spaces), in games (such as chess or Go, where agents have surpassed top human players), in optimizing logistics or energy systems, financial portfolio management, or personalizing recommendations on digital platforms.
For example, in a recommendation system, the agent adjusts suggestions based on user reactions to maximize engagement. In robotics, a robotic arm can learn to grasp various objects, receiving a reward when the grasp is successful.
Main Software Tools, Libraries, Frameworks
Major libraries include OpenAI Gym (simulation environments for RL), Stable Baselines3 (standard RL algorithms), Ray RLlib (large-scale distributed training), TensorFlow Agents, Keras-RL, and Dopamine (by Google).
These tools provide environments, algorithms, and interfaces that facilitate research, prototyping, and deployment of RL solutions in industrial and advanced research contexts.
Recent Developments, Evolutions and Trends
RL has seen major advances with the emergence of model-based approaches, deep RL (combining reinforcement learning with deep learning), and integration with imitation learning techniques. Recent work also focuses on robustness, training efficiency, generalization to varied environments, and reducing data requirements through simulated worlds.
Trends include application to complex autonomous systems (vehicles, drones), industrial automation, and integration with other AI paradigms to create more adaptive and reliable agents.