Hierarchical Reinforcement Learning Controller for 3D Environments
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Solution Overview
Problem
Existing hierarchical reinforcement learning agents struggle to perform effectively in visually complex, partially observable 3D environments, and rely on expert-generated data for goal representations.
Innovation Solution
A hierarchical controller system that uses goal-conditioning techniques to train low-level and high-level neural networks, allowing the agent to learn goal-conditioned behaviors from any experience and generalize across tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hierarchical controller uses goal representations from expert-generated data, then training is simplified, but adaptability to new tasks is limited
Solution Approach 1:
The system enables the agent to generate its own goal representations by observing environmental states and inferring objectives autonomously, eliminating dependency on expert-generated data. The hierarchical controller learns to create meaningful goal representations from raw observations through reinforcement learning, allowing adaptation to novel tasks without human intervention.
Solution Approach 2:
The system transitions from using fixed, pre-defined goal representations to dynamically generated goal representations that adapt to different task environments. By changing how goal representations are created (from expert-provided to observation-based), the system achieves both training simplicity and task adaptability.
2Device complexity
If flat (non-hierarchical) techniques are used, then system structure is simpler, but performance on challenging real-world tasks is insufficient
Solution Approach 1:
The control system is divided into hierarchical levels: a high-level controller that generates goal representations and selects actions, and a low-level controller that executes specific actions based on current observations. This segmentation allows each level to specialize in different aspects of task execution, improving overall performance on complex tasks while maintaining manageable system architecture.
Solution Approach 2:
The system adds a temporal dimension to the control hierarchy by introducing options with durations. The high-level controller plans actions that span multiple time steps, while the low-level controller executes them sequentially. This temporal structuring enables the system to handle complex tasks requiring multi-step reasoning and execution.
3Adaptability or versatility
If agent operates in visually complex partially observable 3D environments, then task difficulty increases, but existing hierarchical agents fail to perform effectively
Solution Approach 1:
The system introduces an intermediate representation layer (goal representations) that mediates between the high-level planning and low-level execution. These goal representations serve as compressed summaries of complex environmental states, enabling the agent to reason about and navigate visually complex 3D environments effectively.
Solution Approach 2:
The hierarchical controller is designed to handle multiple types of tasks and environments universally. The same high-level and low-level controller architecture can adapt to different task domains (e.g., navigation, manipulation, inspection) by learning new goal representations from observations, making the system effective across diverse challenging environments.
Data Source
AI summary
The invention describes a system and a method for controlling an agent interacting with an environment to perform a task, the method comprising, at each of a plurality of first time steps from a plurality of time steps: receiving an observation characterizing a state of the environment at the first time step; determining a goal representation for the first time step that characterizes a goal state of the environment to be reached by the agent; processing the observation and the goal representation using a low-level controller neural network to generate a low-level policy output that defines an action to be performed by the agent in response to the observation, wherein the low-level controller neural network comprises: a representation neural network configured to process the observation to generate an internal state representation of the observation, and a low-level policy head configured to process the state observation representation and the goal representation to generate the low-level policy output; and controlling the agent using the low-level policy output.