Dynamic Deep-Reinforcement-Learning Algorithm in Partially Observable Markov Decision Processes
This work addresses a domain-specific problem for reinforcement learning practitioners dealing with hidden states in real-world implementations, representing an incremental advancement.
The study tackled the challenge of time-varying disturbances in reinforcement learning by introducing three novel architectures that incorporate action trajectories into Recurrent Neural Networks for Partially Observable Markov Decision Processes, with H-TD3 showing potential computational time improvements while maintaining performance.
Recent studies have greatly improved reinforcement learning, and an increased interest in real-world implementation has emerged. In many cases, the implementation is challenged by time-varying disturbances as it introduces hidden states, which makes the problem best described with Partially Observable Markov Decision Processes. An effective approach to address this problem is to introduce a Recurrent Neural Network (RNN) in place of a state estimator. However, only a few studies have investigated the types of information to be supplied to the RNN and the network architecture to handle them. This study discusses the effectiveness of the inclusion of action along with observation and the impact of network architecture to handle them by providing interpretations of how the trajectories are summarized at LSTM networks. Specifically, three novel approaches with different architectures are introduced. All algorithms demonstrated the effectiveness of the inclusion of the action trajectories in simulation environments. In particular, one of the developed algorithms, H-TD3, differs from the typical actor and critic network as the critic network is trained by utilizing the hidden states generated by the actor network as the summarized trajectory information. This novel approach exhibited the potential improvement of the computational time while maintaining the performance.