Thuật ngữ AI
Từ điển đầy đủ về Trí tuệ nhân tạo
Multimodal Imitation Learning
Learning paradigm where the agent learns from demonstrations presenting multiple valid solutions for the same task, capturing the diversity of expert behaviors.
Multimodal Policy Distribution
Probabilistic representation of a policy containing multiple distinct modes, each corresponding to a different resolution strategy to achieve the same objective.
Action Mode
Specific behavioral configuration within a multimodal distribution, representing a distinct and coherent way of executing a task.
Demonstration Aggregation
Process of intelligently combining learning trajectories from different strategies to build a unified policy preserving multimodality.
Gaussian Mixture for Policies
Parametric model representing a multimodal policy as a weighted combination of multiple Gaussian distributions, each Gaussian corresponding to a behavioral mode.
Trajectory Posterior
Conditional distribution on possible trajectories given an initial state and the objective, capturing uncertainty about the different ways to reach the target.
Behavioral Latent Space
Compressed representation where each point encodes a distinct behavioral strategy, allowing navigation and interpolation between different action modes.
Demonstration Clustering
Automatic grouping technique of similar trajectories to identify and isolate the different behavioral modes present in imitation data.
Modality-Action Decoupling
Architecture where the selection of the behavioral mode and the generation of specific actions are handled by separate components for better multimodal modeling.
Mode Inference
Process of identifying the appropriate behavioral mode to use in a given situation, based on the context and the agent's preferences.
Conditional Policy
Policy that generates actions conditioned on both the current state and a selected mode identifier, allowing for explicit control of the adopted strategy.
Optimal Solution Selection
Mechanism that evaluates and chooses the best solution among several valid alternatives based on criteria such as efficiency, robustness, or contextual constraints.
Behavioral Variance
Statistical measure quantifying the dispersion of actions or trajectories around each mode, distinguishing intra-mode variability from multimodality.
Mode Fusion
Technique combining several behavioral modes to create new hybrid strategies, leveraging the strengths of each individual mode.
Sub-policy Learning
Approach where each mode is modeled as a specialized sub-policy, learned independently before being integrated into a global multimodal architecture.
Solution Hierarchy
Organizational structure of different valid solutions, often based on their complexity, efficiency, or optimal application context.
Mode-guided Exploration
Exploration strategy that maintains consistency with a selected behavioral mode while discovering new valid variations within that mode.
Inter-modal Transfer
Ability to transfer knowledge acquired in one behavioral mode to improve learning or performance in another similar mode.
Multimodal Evaluation
Evaluation metric that considers not only the average performance but also the coverage and quality of the different learned behavioral modes.