Action-Value
Action-Value is a concept in reinforcement learning that represents the expected return or reward of taking a specific action in a given state.
Actor
An actor is a person who portrays characters in performances across theater, film, television, or other media.
Actuator
An actuator is a device that converts electrical, hydraulic, or pneumatic energy into physical motion to control or move mechanisms.
Advantage Function
Advantage Function is a concept in reinforcement learning that measures how much better an action is compared to the average action at a given state.
Afterstate
Afterstate is the condition of a system or environment immediately following an action, used in decision-making processes to evaluate outcomes.
Apprenticeship Learning
Apprenticeship learning is a type of machine learning where an agent learns tasks by observing and imitating expert behavior.
Asynchronous DP
Asynchronous DP is a dynamic programming approach where computations proceed independently without waiting for all previous steps to complete, enabling parallel processing and faster problem-solving.
Backup Diagram
A Backup Diagram is a visual representation that outlines the process and structure of data backup systems within an organization.
Baseline
Baseline is a reference point or standard used for comparison to measure progress or changes in a project, process, or study.
Behavior Cloning
Behavior cloning is a machine learning technique where an agent learns to mimic expert behavior by observing and replicating actions from recorded data.
Bellman Equation
The Bellman Equation is a fundamental recursive formula used in dynamic programming and reinforcement learning to determine the optimal decision-making strategy by breaking down complex problems into simpler subproblems.
Competitive RL
Competitive RL is a branch of reinforcement learning where multiple agents learn and adapt strategies by competing against each other in a shared environment.
Contraction Mapping
Contraction mapping is a function on a metric space that brings points closer together, ensuring a unique fixed point.
Cooperative RL
Cooperative RL is a type of reinforcement learning where multiple agents work together to achieve a shared goal by coordinating their actions and learning strategies.
Counterfactual Regret Minimization
Counterfactual Regret Minimization is an iterative algorithm used in game theory to minimize regret by evaluating hypothetical alternative outcomes and improving decision-making strategies over time.
Credit Assignment Problem
The Credit Assignment Problem is the challenge of determining which actions or events are responsible for a particular outcome in a complex system.
Critic
A critic is a person who evaluates, analyzes, and offers opinions about creative works, performances, or ideas to inform and influence public perception.
Curiosity-Driven Learning
Curiosity-driven learning is an approach where learners pursue knowledge motivated primarily by their natural curiosity and intrinsic interest rather than external rewards or requirements.
Default Policy
Default policy is a predefined set of rules or guidelines that apply automatically when no specific instructions or exceptions are provided.
Dense Reward
Dense reward is a reinforcement learning feedback mechanism where an agent receives frequent, detailed rewards after most or every action.
Differential Game
Differential game is a mathematical framework where multiple players make decisions over time, influencing a system described by differential equations to optimize competing objectives.
Discount Factor
Discount factor is a multiplier used to calculate the present value of future cash flows by accounting for the time value of money.
Dyna-Q
Dyna-Q is a reinforcement learning algorithm that combines direct learning from experience with planning using a learned model of the environment.
Eligibility Traces
Eligibility traces are a temporary memory mechanism in reinforcement learning that helps assign credit to recent actions for future rewards.
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