Hybrid Reward Architecture for Reinforcement Learning (1706.04208v2)

Abstract: One of the main challenges in reinforcement learning (RL) is generalisation. In typical deep RL methods this is achieved by approximating the optimal value function with a low-dimensional representation using a deep network. While this approach works well in many domains, in domains where the optimal value function cannot easily be reduced to a low-dimensional representation, learning can be very slow and unstable. This paper contributes towards tackling such challenging domains, by proposing a new method, called Hybrid Reward Architecture (HRA). HRA takes as input a decomposed reward function and learns a separate value function for each component reward function. Because each component typically only depends on a subset of all features, the corresponding value function can be approximated more easily by a low-dimensional representation, enabling more effective learning. We demonstrate HRA on a toy-problem and the Atari game Ms. Pac-Man, where HRA achieves above-human performance.

• The paper introduces a novel RL framework that decomposes complex rewards into manageable sub-functions to improve learning stability.
• It demonstrates enhanced learning efficiency over traditional DQN methods through concurrent training on simpler value functions.
• The approach leverages domain-specific insights for reward decomposition, enabling robust performance in environments with sparse and high-dimensional rewards.

Hybrid Reward Architecture for Reinforcement Learning: A Structured Approach to Complex Value Functions

The paper introduces a novel reinforcement learning (RL) methodology called Hybrid Reward Architecture (HRA) to efficiently tackle challenges associated with high-dimensional and complex value functions in traditional deep RL techniques. By leveraging a decomposed reward function approach, HRA mitigates the common pitfalls of slow convergence and instability found in methods like Deep Q-Networks (DQN), particularly in environments where the optimal value function is difficult to approximate with low-dimensional representations.

Core Concept and Methodology

HRA is characterized by its unique framework that decomposes the environment's reward function into multiple component reward functions. For each component, a separate value function is learned. This decomposition enables simpler approximation of each value function due to the lower dimensionality associated with each component's feature reliance. By training individual RL agents on each decomposed reward function concurrently, and aggregating their action-values, HRA forms an aggregated action-value estimation, which forms the basis of its policy.

This approach diverges from conventional deep RL techniques, which predominantly rely on a single, complex value function approximation. Instead, HRA opts for a complementarity of multiple simpler value functions as surrogates for target value learning, fostering easier generalization.

Experimental Evaluation

HRA was empirically evaluated on a simple fruit-collection grid world and the more complex Atari game, Ms. Pac-Man. In the fruit-collection domain, HRA demonstrated superior learning efficiency and policy performance compared to a standard DQN architecture, showcasing the impact of reward function decomposition and effective domain knowledge exploitation. More notably, in the Ms. Pac-Man environment, HRA achieved performance levels significantly surpassing existing baselines and human-level scores, even exceeding state-of-the-art agents employing advanced preprocessing, despite training on fewer frames.

Implications and Significance

The results underscore HRA's potential in RL applications where decomposing rewards naturally aligns with problem structure. HRA not only improves learning stability and convergence but also enhances performance through structural decomposability—a concept that holds significant promise in addressing real-world problems with immense state-action spaces and sparse reward structures.

Furthermore, HRA's flexibility in leveraging domain-specific knowledge without relying exclusively on deep architectures introduces a versatile applicability to problems that can benefit from hybrid methods, blending tabular and approximated solutions.

Future Directions and Considerations

The successful implementation and outcome of HRA prompt several avenues for future inquiry. Primarily, research can explore automatic or more universal methods of reward function decomposition, which can provide greater applicability across domains without extensive domain-specific engineering. Moreover, the exploration of hybrid functional decomposition with other forms of structured RL approaches, including hierarchical methods or temporal abstractions, represents a fertile field for extending HRA's efficacy and robustness.

Additionally, given HRA's architecture, future research could investigate integrating sophisticated exploration strategies or uncertainty-aware mechanisms within and across component reward functions, further improving robustness against varied environment dynamics.

In conclusion, HRA offers a compelling paradigm for addressing RL challenges associated with complex value functions through the strategic and structured decomposition of rewards. This approach not only enhances performance in traditional RL environments but also lays the groundwork for more scalable and adaptable RL solutions in diverse, complex domains.