Results for "action mapping"

Estimating robot position within a map.

Fast approximation of costly simulations.

A learning paradigm where an agent interacts with an environment and learns to choose actions to maximize cumulative reward.

A high-priority instruction layer setting overarching behavior constraints for a chat model.

Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls.

Combines value estimation (critic) with policy learning (actor).

Separates planning from execution in agent architectures.

What would have happened under different conditions.

System that independently pursues goals over time.

AI systems that perceive and act in the physical world through sensors and actuators.

RL using learned or known environment models.

Directly optimizing control policies.

Optimizing continuous action sequences.

Reward only given upon task completion.

Imagined future trajectories.

Control shared between human and agent.