Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Abstract: Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain. This model uses a soft, top-down attention mechanism to create a bottleneck in the agent, forcing it to focus on task-relevant information by sequentially querying its view of the environment. The output of the attention mechanism allows direct observation of the information used by the agent to select its actions, enabling easier interpretation of this model than of traditional models. We analyze different strategies that the agents learn and show that a handful of strategies arise repeatedly across different games. We also show that the model learns to query separately about space and content (where' vs.what'). We demonstrate that an agent using this mechanism can achieve performance competitive with state-of-the-art models on ATARI tasks while still being interpretable.

• The paper demonstrates that integrating a top-down soft attention mechanism enhances interpretability in RL agents without sacrificing performance.
• It employs a hybrid convolutional-LSTM model to generate focused, spatial queries that clarify decision-making strategies.
• Empirical results on ATARI games validate that the attention mechanism offers actionable insights into agent operations and forward planning.

Interpretable Reinforcement Learning through Attention Augmented Agents

The paper "Towards Interpretable Reinforcement Learning Using Attention Augmented Agents" introduces an innovative model that enhances the interpretability of decision-making in reinforcement learning (RL). Leveraging mechanisms inspired by recent advances in attention models used for image captioning and question answering, this work implements a top-down soft attention model within an RL agent. The resulting framework not only maintains competitive performance on complex tasks, notably the ATARI game environment, but offers intrinsic insights into the operational mechanics and strategic reasoning adopted by the agent.

Model Overview

Central to the proposed framework is the design of attention-augmented agents. These agents are equipped with an attention head mechanism that operates by querying the environment to highlight task-relevant components. This setup, as outlined in the paper, involves processing input visual data via a convolutional network followed by a recurrent layer, resulting in a keys and values representation. Upon these, spatial positional embeddings are affixed. An LSTM-based policy core subsequently generates queries that direct the attention head to specific parts of the input, forming compressed output vectors critical for decision-making.

Several notable attributes underpin this model:

1. Soft Attention: The model employs spatial softmax for attention distribution, facilitating differentiable end-to-end training.
2. Top-down Queries: Unlike self-attention paradigms where queries depend directly on the input, here queries originate from the LSTM state enabling active, context-driven perception focused on task objectives.
3. Spatial Bottleneck: The design enforces a compact representation through spatial aggregation, ensuring focused strategy formation and interpretability.

Results and Analysis

The comprehensive analysis on ATARI tasks exemplifies the efficacy of these agents in harnessing elements such as player position, enemy tracking, and section-specific focus, manifested through observable attention maps. This methodological choice offers clarity in understanding the dynamic adaptation of strategies.

Key findings include:

• Strategic Diversification: The agent demonstrates consistent attention patterns across various games, reinforcing its ability to discern critical elements, such as enemies and obstacles, and adapt to novel configurations like the introduction of new entities.
• Forward Planning: The ability of agents to scan potential trajectories suggests sophisticated spatial resolution abilities, critical for forward planning tasks inherent in certain games.
• Top-down Influence: Empirical evaluations reflect substantial performance improvements when utilizing the top-down attention structure over bottom-up alternatives, underscoring the significance of higher-order influence in query formulations.

Practical Implications and Future Directions

The interpretability afforded by these attention-augmented agents is substantial, paving the way for deeper exploration into RL decision-making processes. Practically, these insights can facilitate improved debugging and refinement of RL strategies, opening avenues for deploying RL in complex real-world scenarios where understanding agent rationale is crucial.

Theoretically, the interplay between "what" and "where" queries highlights promising directions in exploring multimodal RL architectures and the integration of content-specific spatial awareness, potentially enhancing strategic adaptability. Future research could explore optimizing attention head configurations, expanding the application scope, and refining interpretability tools to leverage the insights from attention dynamics comprehensively.

In summary, the paper successfully demonstrates an approach to balance the complexities of RL with the need for interpretability, thereby contributing valuable perspectives on the nuanced interactions within intelligent agent frameworks. Additionally, the in-depth analytical techniques employed across diverse ATARI environments offer a robust validation platform, setting a benchmark for future endeavors in interpretable RL.