Strategic Attentive Writer for Learning Macro-Actions (1606.04695v1)

Abstract: We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement learning setting. The network builds an internal plan, which is continuously updated upon observation of the next input from the environment. It can also partition this internal representation into contiguous sub- sequences by learning for how long the plan can be committed to - i.e. followed without re-planing. Combining these properties, the proposed model, dubbed STRategic Attentive Writer (STRAW) can learn high-level, temporally abstracted macro- actions of varying lengths that are solely learnt from data without any prior information. These macro-actions enable both structured exploration and economic computation. We experimentally demonstrate that STRAW delivers strong improvements on several ATARI games by employing temporally extended planning strategies (e.g. Ms. Pacman and Frostbite). It is at the same time a general algorithm that can be applied on any sequence data. To that end, we also show that when trained on text prediction task, STRAW naturally predicts frequent n-grams (instead of macro-actions), demonstrating the generality of the approach.

Overview of "Strategic Attentive Writer for Learning Macro-Actions"

The paper "Strategic Attentive Writer for Learning Macro-Actions" introduces an innovative architecture, the Strategic Attentive Writer (STRAW), which employs a deep recurrent neural network capable of developing macro-action policies through reinforcement learning. The main contribution of this work is the STRAW model's ability to build implicit plans, interacting with environments in an end-to-end fashion without prior information, thereby addressing challenges in learning temporally abstracted actions.

Key Features of the STRAW Model

1. Macro-Action Planning: STRAW establishes a framework for learning high-level macro-actions that extend over varying time lengths. Unlike traditional reinforcement learning methods that output single actions at each time step, STRAW maintains a multi-step action plan, updating it periodically based on the observed environment. These macro-actions enhance exploration efficiency and computational economy by enabling the agent to commit to a plan, thus reducing the frequency of replanning.
2. Partitioning Strategies: STRAW employs a novel mechanism for deciding when to commit to an action plan and when to replan, allowing it to operate efficiently over long time horizons. The model predicts when to terminate a macro-action and updates plans accordingly, based on learned patterns within the data.
3. General Applicability: Beyond reinforcement learning, STRAW demonstrates versatility by performing effectively in sequence prediction tasks. A notable example is its performance in text prediction, where it predicts frequent n-grams, showcasing its potential utility across different data types.

Experimental Evaluation

The performance of STRAW is validated through extensive experiments in various domains, notably ATARI games, 2D maze navigation, and text prediction tasks. In ATARI games, STRAW and its variant with structured exploration (STRAWe) exhibit superior performance against baselines, particularly in games like Ms. Pacman and Frostbite, where strategies requiring long-term planning are advantageous.

• ATARI Games:

STRAW and STRAWe outperform traditional feedforward and LSTM-based models in games that require complex strategic planning. The STRAW architecture leverages macro-actions to achieve efficient exploration, demonstrated by substantial improvements in game scores.

• 2D Maze Navigation:

The model learns to identify key exploratory strategies, such as corridor navigation and cornering, through macro-actions, resulting in efficient goal-reaching policies.

• Text Prediction:

By learning to use frequent sequences like n-grams as macro-actions, STRAW provides evidence of its capability to generalize to non-RL tasks, supporting its broader applicability.

Implications and Future Directions

The architecture proposed in this work addresses significant limitations in traditional reinforcement learning by enabling the discovery of temporal abstractions without explicit supervision or hand-crafted subgoals. This capability enhances both the scalability and adaptability of AI models deployed in dynamic, high-complexity environments.

Future research directions inspired by STRAW could explore:

• Further integration with deep learning advancements for better prediction and abstraction capabilities.
• Expanding the framework to accommodate a wider array of environments, including complex real-world applications.
• Investigating the integration of STRAW with hierarchical reinforcement learning to refine decision-making processes across different levels of abstraction.

In conclusion, the Strategic Attentive Writer model presents a substantial advancement in the domain of learning complex temporal patterns, offering insights and methodologies that could drive future innovations in AI models requiring strategic planning and efficient exploration. This research lays the groundwork for more adaptive and robust AI systems capable of automating strategic decision-making processes in dynamic environments.