RL Action
RL Action is a decision or move taken by an agent within a Reinforcement Learning environment to maximize rewards and achieve specific goals.
RL Agent
An RL Agent is a software entity that learns to make decisions by interacting with an environment using reinforcement learning principles.
RL Environment
RL Environment is the setting or system within which a reinforcement learning agent interacts, making decisions and receiving feedback to learn optimal behavior.
RL Horizon
RL Horizon is the planning timeframe in reinforcement learning that defines how far into the future an agent considers its actions to maximize cumulative rewards.
RL Policy
RL Policy is a set of rules or guidelines that an agent follows to make decisions in reinforcement learning environments.
RL Reward
RL Reward is a feedback signal in reinforcement learning that quantifies the success of an agent's actions toward achieving a goal.
RL State
RL State is the representation of an environment's current situation used by a reinforcement learning agent to make decisions.
Rollout
Rollout is the process of gradually introducing a new product, feature, or update to users or customers in a controlled and strategic manner.
Rollout Policy
Rollout Policy is a planned strategy that governs how new software features or updates are gradually introduced to users.
SARSA
SARSA is a reinforcement learning algorithm that updates its action-value estimates based on the current state, action, reward, next state, and next action.
Selection
Selection is the process of choosing the most suitable option from a set of alternatives based on specific criteria or goals.
Sensor
A sensor is a device that detects and measures physical or environmental changes and converts them into signals for monitoring or control.
Simulation-Based Search
Simulation-Based Search is a problem-solving technique that uses computer simulations to explore and optimize complex decision-making processes.
Skill
Skill is the ability to perform tasks effectively and efficiently through knowledge, practice, and experience.
Softmax Exploration
Softmax Exploration is a strategy in reinforcement learning that selects actions probabilistically based on their estimated values, promoting a balance between exploration and exploitation.
Sparse Reward
Sparse reward is a type of feedback in reinforcement learning where signals are given infrequently, often only after a series of actions or upon task completion.
State-Value
State-value is a function that estimates the expected return or future rewards achievable from a given state in a decision-making process.
Stochastic Game
Stochastic game is a strategic game model where outcomes depend on probabilistic transitions between states and the decisions of multiple players.
Structural Credit Assignment
Structural Credit Assignment is the process of determining the contribution of individual components within a complex system to the overall outcome or performance.
Subgoal
A subgoal is a smaller, manageable objective set within a larger goal to help systematically achieve the main outcome.
TD(0)
TD(0) is a fundamental reinforcement learning algorithm that updates value estimates using immediate rewards and the estimated value of the next state.
TD(lambda)
TD(lambda) is a reinforcement learning algorithm that combines temporal difference learning with eligibility traces to efficiently predict future rewards.
Temporal Credit Assignment
Temporal credit assignment is the process of determining which past actions or decisions led to current outcomes, especially when rewards or results are delayed over time.
Temporal Difference Learning
Temporal Difference Learning is a reinforcement learning method where an agent learns to predict future rewards by updating estimates based on the difference between successive predictions over time.
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