Scalability

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Reinforcement Learning

Sascha Block2025-01-26T18:51:25+01:00Tags: Actor-Critic Models, algorithmic trading, AlphaGo, autonomous vehicles, computational complexity, Deep Q-Networks, DQN, energy optimization, ethics in AI, Exploration vs. exploitation, Google Dopamine, Markov Decision Process, MDP, OpenAI Five, OpenAI Gym, overfitting, Policy-Based Methods, Portfolio Optimization, PyTorch, Q-Learning, Reinforcement Learning, Reward Design, reward maximization, RL, RLlib, Robotics, safety in AI, Scalability, Smart grids, sparse rewards, Stable baselines, Supervised Learning, TensorFlow, Unsupervised Learning|

Reinforcement learning is a branch of machine learning that trains agents to make optimal decisions by interacting with their environment. Reinforcement learning is used in autonomous vehicles, robotics, games such as AlphaGo and AlphaZero and the optimization of resources in energy systems.

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