Results for "energy function"

Variable whose values depend on chance.

Approximating expectations via random sampling.

Restricting updates to safe regions.

Maximizing reward without fulfilling real goal.

Ensuring learned behavior matches intended objective.

Willingness of system to accept correction or shutdown.

Model behaves well during training but not deployment.

Asking model to review and improve output.

Enables external computation or lookup.

Applying learned patterns incorrectly.

Loss of old knowledge when learning new tasks.

Using output to adjust future inputs.

System returns to equilibrium after disturbance.

Learning physical parameters from data.

Predicts next state given current state and action.

Directly optimizing control policies.

Reward only given upon task completion.

Learning action mapping directly from demonstrations.

Optimal pathfinding algorithm.

Sampling-based motion planner.

Planning via artificial force fields.

Learning by minimizing prediction error.

Predicting borrower default risk.

Agents optimize collective outcomes.

Tendency to gain control/resources.

Goals useful regardless of final objective.

Updating a pretrained model’s weights on task-specific data to improve performance or adapt style/behavior.