Reinforcement Learning
Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment. It has achieved breakthroughs in game playing, robotics, and AI alignment.
Understanding Reinforcement Learning
Reinforcement learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment, receiving reward or penalty signals that guide it toward optimal behavior over time. Unlike supervised learning, which requires labeled examples, RL discovers strategies through trial and error, making it suitable for problems where the correct action is not known in advance. Landmark RL achievements include DeepMind's AlphaGo defeating world champion Go players, OpenAI's Dota 2 bots, and robotic manipulation systems that learn dexterous tasks from scratch. Key concepts include the policy that maps states to actions, the value function that estimates future rewards, and the balance between exploration of new strategies and exploitation of known good ones. Reinforcement learning has become crucial in AI alignment through reinforcement learning from human feedback, which uses human preferences to train reward models that guide language model behavior.
Category
Reinforcement Learning
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Reinforcement Learning Terms
Deep Reinforcement Learning
Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms to handle complex, high-dimensional environments. It has achieved superhuman performance in games like Go, chess, and Atari.
Exploration vs Exploitation
Exploration vs exploitation is a fundamental dilemma in reinforcement learning between trying new actions to discover better rewards versus leveraging known good actions. Balancing both is key to optimal long-term performance.
Imitation Learning
Imitation learning is a technique where an AI agent learns to perform tasks by observing and mimicking expert demonstrations. It bridges the gap between supervised learning and reinforcement learning.
Inverse Reinforcement Learning
Inverse reinforcement learning infers the reward function that an expert is optimizing by observing their behavior. It enables AI systems to learn goals and preferences from demonstrations.
Markov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making problems with probabilistic outcomes. MDPs are the formal foundation for reinforcement learning algorithms.
Minimax
Minimax is a decision-making algorithm used in adversarial settings where one player tries to maximize their score while the other minimizes it. It is the classical approach for game-playing AI systems.
Policy
A policy in reinforcement learning is a function that maps states to actions, defining the agent's behavior strategy. The goal of RL is to learn an optimal policy that maximizes cumulative reward.
Q-Learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of actions in states to find an optimal policy. It uses a Q-table or neural network to estimate expected cumulative rewards for each state-action pair.