Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning (1706.05064v2)

Abstract: As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequences of instructions after learning useful skills that solve subtasks. In this problem, we consider two types of generalizations: to previously unseen instructions and to longer sequences of instructions. For generalization over unseen instructions, we propose a new objective which encourages learning correspondences between similar subtasks by making analogies. For generalization over sequential instructions, we present a hierarchical architecture where a meta controller learns to use the acquired skills for executing the instructions. To deal with delayed reward, we propose a new neural architecture in the meta controller that learns when to update the subtask, which makes learning more efficient. Experimental results on a stochastic 3D domain show that the proposed ideas are crucial for generalization to longer instructions as well as unseen instructions.

• The paper introduces a novel approach that integrates parameterized skills with a meta-controller to enable zero-shot task generalization in complex environments.
• The methodology uses an analogy-making objective to improve skill acquisition and effectively manage delayed rewards in a stochastic 3D setting.
• Experimental results demonstrate that the proposed system outperforms traditional RL architectures, highlighting its potential for robust autonomous applications.

Overview of Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning

The paper "Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning" by Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli explores an approach to advance zero-shot task generalization in reinforcement learning (RL). It proposes a novel problem setting where agents must execute sequences of instructions without seeing the instructions beforehand, combining skill acquisition and instruction execution within a stochastic 3D environment.

Key Problem and Challenges

The problem centers on executing a given list of instructions efficiently while facing certain RL challenges. These include the need for generalization to unseen instructions and longer sequences of instructions, handling delayed reward signals, addressing unexpected environmental events, and maintaining a coherent internal memory to deal with repeating tasks. The agent must operate optimally despite these constraints, which push the capabilities of current RL methods.

Proposed Architecture

The proposed solution divides the learning problem into two principal tasks: learning parameterized skills to handle numerous subtasks and learning a meta-controller to combine these skills into executing complex instruction sequences. The parameterized skill uses a unique analogy-making objective, encouraging the network to draw parallels between similar tasks, thereby facilitating generalization. In parallel, the meta-controller leverages a hierarchical architecture featuring a novel neural network design to handle delayed rewards efficiently and decide when to switch tasks.

Experimental Evaluation

Experiments conducted within a 3D Minecraft-inspired environment demonstrate the efficacy of the proposed architecture. Through comprehensive tests, including unseen and lengthier instruction sequences, the agent showed a significant capacity for zero-shot task generalization. The system outperformed traditional RL architectures by utilizing a meta-controller capable of dynamic decision-making, particularly impressive in handling longer instructions and unforeseen scenarios.

Impact and Future Directions

The implications of this research are manifold both practically and theoretically. Practically, the proposed architecture may significantly enhance the robustness and adaptability of autonomous systems, such as home robots executing variable user commands without retraining. Theoretically, this work paves the way for further exploration of hierarchical architectures in RL and their ability to integrate high-level decision-making with low-level skill execution seamlessly. Future research could explore extending such architectures to broader tasks and more sophisticated forms of instruction or environments, potentially enhancing the versatility and scalability of autonomous RL agents.

This paper contributes a robust framework for addressing the complexities of zero-shot learning and instruction execution in RL, showcasing successful generalization in a challenging, stochastic domain.