Results for "hostile environment"

Modeling environment evolution in latent space.

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

Maintaining two environments for instant rollback.

Artificial environment for training/testing agents.

RL using learned or known environment models.

Learned model of environment dynamics.

Perceived actions an environment allows.

Ability to replicate results given same code/data; harder in distributed training and nondeterministic ops.

Strategy mapping states to actions.

Continuous cycle of observation, reasoning, action, and feedback.

Separates planning from execution in agent architectures.

Simultaneous Localization and Mapping for robotics.

Interleaving reasoning and tool use.

Agent reasoning about future outcomes.

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

External sensing of surroundings (vision, audio, lidar).

RL without explicit dynamics model.

Finding routes from start to goal.

Planning via artificial force fields.

Detecting and avoiding obstacles.

Internal representation of environment layout.

Isolating AI systems.

A mismatch between training and deployment data distributions that can degrade model performance.

Methods to protect model/data during inference (e.g., trusted execution environments) from operators/attackers.

A system that perceives state, selects actions, and pursues goals—often combining LLM reasoning with tools and memory.

All possible configurations an agent may encounter.

Set of all actions available to the agent.

Learning only from current policy’s data.

Simple agent responding directly to inputs.

Maintaining alignment under new conditions.