Dynamic Deep-Reinforcement-Learning Algorithm in Partially Observable Markov Decision Processes

Novel RNN Architectures for Robust RL in Partially Observable Environments

In a significant advance for applied reinforcement learning (RL), new research tackles a core challenge in real-world deployment: handling time-varying disturbances that create hidden states. A preprint paper (arXiv:2307.15931v2) introduces and analyzes three novel neural network architectures that effectively integrate action history with observations, providing a robust solution for problems best framed as Partially Observable Markov Decision Processes (POMDPs). The study offers critical insights into how Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, can summarize trajectory information to overcome partial observability, with one algorithm demonstrating a path to reduced computational cost without sacrificing performance.

The POMDP Challenge and the RNN Solution

While reinforcement learning has seen tremendous progress in simulated environments, real-world implementation is often hindered by non-stationary dynamics and incomplete state information. These time-varying disturbances mean an agent cannot fully perceive the true state of the environment, turning the problem into a POMDP. A standard and effective approach is to replace a traditional state estimator with an RNN, which can maintain an internal memory or hidden state to infer missing information from historical data. However, prior research has left key questions unanswered regarding what specific data the RNN should process and the optimal network design to process it.

This research directly addresses that gap by systematically investigating the value of feeding action trajectories—the history of actions taken—into the RNN alongside the observation history. The authors provide novel interpretations of how LSTM networks summarize these combined trajectories, moving beyond treating the RNN as a black box. Their analysis confirms that explicitly including past actions provides a richer context for the agent, leading to more stable and effective learning in partially observable settings.

Introducing Three Novel Architectural Approaches

The core contribution of the work is the proposal and evaluation of three distinct neural network architectures designed to leverage both observation and action history:

• Architecture A & B: These designs explore different methods of concatenating or processing observation and action sequences before feeding them into the LSTM layers, testing how the fusion of information impacts the network's ability to build a useful hidden state.
• H-TD3 (Hybrid TD3): This represents the most innovative departure from standard actor-critic RL. Typically, actor and critic networks are separate, each maintaining their own RNN to summarize history. In H-TD3, the critic network is trained by utilizing the hidden states generated by the actor network as the summarized trajectory information.

This architectural shift in H-TD3 is significant. By sharing the actor's hidden state with the critic, the algorithm reduces redundancy, as the computationally expensive process of summarizing the trajectory is performed only once. All three algorithms were validated in simulation environments, consistently demonstrating the superior performance gained from the inclusion of action history.

Why the H-TD3 Algorithm Matters for Real-World RL

The H-TD3 algorithm emerged with particularly promising implications for efficient real-world learning. The results indicated that this hybrid approach maintained performance on par with or better than conventional dual-network architectures while exhibiting a clear potential for improved computational efficiency.

This efficiency gain is critical for scaling RL to physical systems like robotics or industrial control, where processing power and latency are constrained. By eliminating the need for a separate, parallel recurrent pathway in the critic, H-TD3 reduces the number of trainable parameters and the required forward passes per time step. The research suggests this architectural innovation could make robust, memory-based RL more feasible for deployment on edge devices and in time-sensitive applications.

Key Takeaways for AI and Robotics Development

• Action History is Critical: For RL agents operating in partially observable, real-world environments, explicitly providing past actions to the RNN is as important as providing past observations for building an accurate internal state.
• Architecture Drives Efficiency: How observation and action histories are architecturally integrated into the RNN significantly impacts both performance and computational load. The H-TD3 model presents a novel, more efficient paradigm for actor-critic methods.
• Path to Practical Deployment: By designing networks that minimize redundant computation—like sharing the actor's hidden state with the critic—researchers can create RL agents that are both robust to disturbances and efficient enough for real-time control systems.

This study provides a principled framework and novel tools for advancing RL beyond the lab, offering concrete architectural solutions to the pervasive problem of hidden states caused by real-world noise and dynamics.