MLG 029 Reinforcement Learning Intro

29 snips

Feb 5, 2018

How does an AI learn to trade, play games, or control a robot without flash cards? Explore reinforcement learning, where agents learn through actions and rewards, from model-free reactions to model-based planning. The discussion also compares policy-gradient and value-based methods, surveys popular tools and training environments, and visits the big questions around reasoning and AI.

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INSIGHT

RL Learns By Interaction Not Labels

Reinforcement learning (RL) trains an agent by rewards and punishments instead of labeled examples.
RL focuses on action and goal achievement through interactions with an environment.

INSIGHT

Action Is The Heart Of AI

Action capability is central to artificial intelligence according to the host's checklist.
Reinforcement learning provides the action component that complements perception and learning.

INSIGHT

Model-Based RL Bridges To General AI

Model-based RL introduces planning and moves closer to broader AI objectives.
Combining perception, learning and planning approaches advances toward general AI.

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Notes and resources: ocdevel.com/mlg/29

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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.

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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.