Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes (1805.04419v1)

Abstract: In recent years, reinforcement learning has achieved many remarkable successes due to the growing adoption of deep learning techniques and the rapid growth in computing power. Nevertheless, it is well-known that flat reinforcement learning algorithms are often not able to learn well and data-efficient in tasks having hierarchical structures, e.g. consisting of multiple subtasks. Hierarchical reinforcement learning is a principled approach that is able to tackle these challenging tasks. On the other hand, many real-world tasks usually have only partial observability in which state measurements are often imperfect and partially observable. The problems of RL in such settings can be formulated as a partially observable Markov decision process (POMDP). In this paper, we study hierarchical RL in POMDP in which the tasks have only partial observability and possess hierarchical properties. We propose a hierarchical deep reinforcement learning approach for learning in hierarchical POMDP. The deep hierarchical RL algorithm is proposed to apply to both MDP and POMDP learning. We evaluate the proposed algorithm on various challenging hierarchical POMDP.

• The paper proposes the hDRQN algorithm, which integrates deep learning and recurrent networks into hierarchical reinforcement learning to effectively handle partially observable environments (POMDPs).
• Experimental results show hDRQN algorithms outperform flat and other hierarchical RL methods in partially observable domains, highlighting the importance of RNNs for capturing temporal dependencies.
• The algorithm demonstrates scalability and efficiency for complex tasks under partial observability, showing promise for practical applications like robotics and autonomous navigation in uncertain environments.

Deep Hierarchical Reinforcement Learning in POMDPs

The paper under review presents a comprehensive paper on enhancing hierarchical reinforcement learning (HRL) by integrating deep learning techniques to address complex tasks framed as Partially Observable Markov Decision Processes (POMDPs). The authors propose an innovative algorithm, Hierarchical Deep Recurrent Q-Networks (hDRQN), which extends traditional reinforcement learning (RL) approaches to accommodate both the requirements of hierarchical task structures and environments characterized by partial observability.

Reinforcement learning has achieved substantial progress, particularly when models incorporate deep learning frameworks. However, many classical RL algorithms falter in scenarios demanding hierarchical task handling, especially when the state information is not fully observable. Standard flat RL methods often struggle with such complexities due to their inability to process hierarchical subtasks efficiently. HRL, characterized by its decomposing approach to complex tasks, offers a strategic resolution by leveraging decomposed subproblems. Coupling this with the versatility of deep learning provides a significant leap in tackling partially observable environments.

The Hierarchical Deep Recurrent Q-Networks Algorithm

The proposed hDRQN algorithm is designed to function effectively within POMDP settings, utilizing a two-tier hierarchical policy structure. This structure includes a top-level policy, employed to determine subgoals, and a lower-level policy responsible for achieving these subgoals through primitive actions. This hierarchical framework is reinforced by incorporating recurrent neural networks (RNNs) to address the challenges of partial observability. RNNs assist in maintaining a memory of observed states, enabling the agent to accumulate a broader context from historical data.

The hDRQN algorithm is realized through two distinct frameworks: hDRQNv1 and hDRQNv2. The distinction between them lies in the input to the meta-controller; in hDRQNv1, it utilizes the initial observations, whereas hDRQNv2 relies on the hidden states generated by the sub-controller. This differential architecture leverages the recurrent features of RNNs more robustly, as evidenced by their experimental evaluations, which illustrate the relative performance benefits offered by hDRQNv2.

Key Experimental Insights

The authors conduct thorough experimental analysis across various domains, including multiple-goals tasks within gridworld environments, the challenging four-rooms domain, and the complex ATARI 2600 Montezuma's Revenge game. These experiments reveal crucial insights:

1. Effectiveness of Hierarchical Approach: The hDRQN algorithms consistently outperform standard flat RL algorithms such as Deep Q-Networks (DQN) and its recurrent version (DRQN), as well as hierarchical RL algorithms like hDQN when applied to partially observable domains.
2. Advantage of Recurrent Networks: The integration of RNNs in capturing temporal dependencies is critical, especially in POMDPs. This capability facilitates improved decision-making in contexts constructed from sequences of observations rather than point estimates.
3. Scalability and Efficiency: The experimental design attests to the scalability and efficiency of hDRQN, stemming from its ability to operate with partial observability and navigate complex hierarchical tasks effectively.

Implications and Future Directions

The approach advocated in this paper holds substantial promise for applications where decision-making processes necessitate comprehension of intricate task hierarchies under uncertainty. Practically, such methods could revolutionize domains like autonomous navigation, robotics, and strategic planning in unknown and dynamic environments.

From a theoretical standpoint, the decomposition of RL tasks into hierarchical structures presents a fertile ground for future research, especially when paired with advanced neural architectures capable of robust state representation. However, the research also highlights current limitations, including the static nature of predefined subgoal sets and the restriction to two-level hierarchical frameworks. Future work will benefit from exploring dynamic subgoal discovery and deploying multi-tier hierarchical models to further enhance adaptability and performance.

The paper offers a significant contribution to the landscape of hierarchical reinforcement learning in partially observable settings, providing a robust framework that is both theoretically innovative and practically relevant. As AI continues to evolve, such frameworks will be pivotal in elevating the capabilities of agents to interact with their environments more intelligently and efficiently.