Relational Deep Reinforcement Learning (1806.01830v2)

Abstract: We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL.

• The paper presents a new deep RL approach that integrates relational reasoning to improve sample efficiency and achieve zero-shot transfer.
• It demonstrates superior performance on the Box-World task and six StarCraft II mini-games, even surpassing human grandmaster levels in four games.
• The method leverages multi-head dot-product attention and structured perceptual reasoning to model abstract relations in dynamic, complex environments.

An Examination of Relational Deep Reinforcement Learning

The paper "Relational Deep Reinforcement Learning" proposes a novel approach to deep reinforcement learning (RL) that seeks to address the limitations observed in traditional deep RL models, specifically their low sample efficiency and poor generalization to slight task variations. This is achieved by introducing a structured perception mechanism and relational reasoning capabilities into deep RL frameworks. The research demonstrates these capabilities through the development of an agent that utilizes self-attention mechanisms to evaluate relationships between different entities in an environment, thereby guiding a model-free RL policy.

Core Contributions

The key contributions of this paper are multifaceted. Firstly, the authors developed an RL task named Box-World to explicitly require and assess relational reasoning. The results illustrate that agents incorporating relational representation capabilities through non-local computation, based on attention models, exhibit superior generalization compared to those lacking such capabilities. Secondly, the application of the proposed architecture to the complex environment of StarCraft II demonstrates state-of-the-art performance on six mini-games, exceeding human grandmaster performance on four.

Architecture and Technical Innovations

The design architecture capitalizes on a novel agent equipped with relational inductive biases intended to optimize the learning of entangling relations. Central to this design is the use of multi-head dot-product attention (MHDPA) for the computation of non-local interactions between entities, allowing the modeling of pairwise and more complex relations. The agents are enhanced with structured perceptual reasoning modules that flexibly process scene representations while iteratively computing higher-order relations grounded in message-passing-like architectures.

In terms of architecture, entities are implicitly defined by local features extracted from convolutional networks, with supplementary positional information to facilitate location-based referencing. Importantly, the components of this architecture are carefully delineated to facilitate effective exploration, ensuring increased sample efficiency and robustness in dynamic and combinatorially complex environments.

Experimental Findings

The experiments conducted across both Box-World and StarCraft II mini-games provide evidence for the theoretical claims. The implementation of relational agents in Box-World illustrates strong performance with demonstrable zero-shot transfer capabilities to more intricate scenarios than those encountered during training. Such performance particularly underscores the architectural foundation's ability to generalize from limited data.

The results from StarCraft II mini-games further validate the practical capabilities of this architecture. The relational agent demonstrates superior performance relative to existing architectures and baseline controls under the SC2LE framework. These results suggest an improved understanding of abstract relations among game elements, attributed to the use of hierarchical attention mechanisms strategically integrated into the RL framework.

Theoretical and Practical Implications

This research propels the understanding of deep RL by merging relational learning insights with the functional capacity of deep models, manifested in structured internal representations and enhanced reasoning capabilities. Practically, the findings suggest potential applications in environments demanding abstract and hierarchical decision-making processes, such as strategic games and complex real-world navigation tasks.

Potential future developments in AI could involve integrating more sophisticated structured perceptual mechanisms and exploring the synergy of hierarchical RL and planning frameworks. Expanding on these models might elucidate further design principles for task-general RL agents that efficiently harness abstract, relational knowledge.

In summary, this paper makes substantial strides in advancing deep reinforcement learning methodologies by embedding structured relational reasoning within neural architectures, thereby offering a robust foundational framework for further exploration and application across diversified domains.