MLG 029 Reinforcement Learning Intro

Notes and resources: ocdevel.com/mlg/29 Try a walking desk to stay healthy while you study or work! Reinforcement Learning (RL) is a fundamental component of artificial intelligence, different from purely being AI itself. It is considered a key aspect of AI due to its ability to learn through interactions with the environment using a system of rewards and punishments. Links: openai/baselines reinforceio/tensorforce NervanaSystems/coach rll/rllab Differential Computers Concepts and Definitions Reinforcement Learning (RL): • • RL is a framework where an "agent" learns by interacting with its environment and receiving feedback in the form of rewards or punishments. • It is part of the broader machine learning category, which includes supervised and unsupervised learning. • Unlike supervised learning, where a model learns from labeled data, RL focuses on decision-making and goal achievement. • Comparison with Other Learning Types Supervised Learning: • • Involves a teacher-student paradigm where models are trained on labeled data. • Common in applications like image recognition and language processing. • Unsupervised Learning: • • Not commonly used in practical applications according to the experience shared in the episode. • Reinforcement Learning vs. Supervised Learning: • • RL allows agents to learn independently through interaction, unlike supervised learning where training occurs with labeled data. • Applications of Reinforcement Learning Games and Simulations: • • Deep reinforcement learning is used in games like Go (AlphaGo) and video games, where the environment and possible rewards or penalties are predefined. • Robotics and Autonomous Systems: • • Examples include robotics (e.g., Boston Dynamics mules) and autonomous vehicles that learn to navigate and make decisions in real-world environments. • Finance and Trading: • • Utilized for modeling trading strategies that aim to optimize financial returns over time, although breakthrough performance in trading isn’t yet evidenced. • RL Frameworks and Environments Framework Examples: • • OpenAI Baselines, TensorForce, and Intel's Coach, each with different capabilities and company backing for development. • Environments: • • OpenAI's Gym is a suite of environments used for training RL agents. • Future Aspects and Developments Model-based vs. Model-free RL: • • Model-based RL involves planning and knowledge of the world dynamics, while model-free is about reaction and immediate responses. • Remaining Challenges: • • Current hurdles in AI include reasoning, knowledge representation, and memory, where efforts are ongoing in institutions like Google DeepMind for further advancement. •