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.
Understanding Policy
A policy in reinforcement learning is a strategy or mapping that defines what action an agent should take in each possible state of its environment. Policies can be deterministic, always selecting the same action for a given state, or stochastic, providing a probability distribution over possible actions. The goal of reinforcement learning algorithms like Q-learning and policy gradient methods is to discover an optimal policy that maximizes the cumulative reward over time. In robotics, a policy might dictate motor commands based on sensor readings; in game-playing AI like AlphaGo, it determines which move to make given the current board position. Deep reinforcement learning represents policies using neural networks that can handle high-dimensional state spaces. Policy optimization is central to training modern AI systems, including the use of reinforcement learning from human feedback to align large language models with human preferences.
Category
Reinforcement Learning
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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.
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.
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.