Deep Reinforcement Learning (DRL) is a field of artificial intelligence that combines classical reinforcement learning with deep neural networks. It involves training an agent to make sequential decisions in complex environments, where the agent learns to maximize a cumulative reward. DRL differs from other machine learning methods in that it is not supervised by correct output examples; instead, it learns through trial and error by interacting with its environment. This approach enables the handling of problems with very large or continuous state and action spaces, where traditional methods fall short.
Use cases and examples
DRL is used in robotics control, allowing machines to learn complex tasks such as object manipulation and locomotion. It is also applied in video games (AlphaGo, Dota 2), financial portfolio management, optimizing communication networks, smart building energy management, and strategy design for autonomous vehicles.
Main software tools, libraries, frameworks
Key DRL tools include TensorFlow Agents, Stable Baselines3, RLlib (Ray), OpenAI Baselines, and Keras-RL. These libraries offer ready-to-use implementations of main algorithms such as DQN, PPO, A3C, DDPG, or SAC, facilitating the design, training, and evaluation of agents in simulated or real environments.
Recent developments, evolutions, and trends
Recent research focuses on improving learning stability, generalization to unknown environments, and reducing training data requirements. Integrating DRL with imitation learning, meta-learning, and multi-agent learning is opening new perspectives, as is applying DRL to real-world, complex, dynamic environments. Trends also include increased computational efficiency and broader access through open-source platforms.