Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment (2312.03263v3)

Abstract: Optimal decision-making presents a significant challenge for autonomous systems operating in uncertain, stochastic and time-varying environments. Environmental variability over time can significantly impact the system's optimal decision making strategy for mission completion. To model such environments, our work combines the previous notion of Time-Varying Markov Decision Processes (TVMDP) with partial observability and introduces Time-Varying Partially Observable Markov Decision Processes (TV-POMDP). We propose a two-pronged approach to accurately estimate and plan within the TV-POMDP: 1) Memory Prioritized State Estimation (MPSE), which leverages weighted memory to provide more accurate time-varying transition estimates; and 2) an MPSE-integrated planning strategy that optimizes long-term rewards while accounting for temporal constraint. We validate the proposed framework and algorithms using simulations and hardware, with robots exploring a partially observable, time-varying environments. Our results demonstrate superior performance over standard methods, highlighting the framework's effectiveness in stochastic, uncertain, time-varying domains.

• The paper introduces a novel TV-POMDP framework that models dynamic transitions for improved decision-making under uncertainty.
• It employs Memory Prioritized State Estimation (MPSE) to weigh observations, enhancing state estimation and planning accuracy.
• Simulations and real-world tests on autonomous vehicles demonstrate the new approach outperforms traditional methods.

Introduction to Time-Varying POMDPs

The optimization of decision-making for autonomous systems is significantly challenged by uncertain and dynamic environments. Traditional frameworks, such as Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs), have limitations in modeling environments where conditions change unpredictably over time. The paper introduces an innovative approach to address such challenges in autonomous system navigation and planning.

New Framework: TV-POMDP

The researchers propose a Time-Varying POMDP (TV-POMDP) that incorporates dynamic probability functions to capture transitions without leading to an explosion of the state space. This framework accounts for the fact that outcomes of the same action can differ depending on when it is executed, meaning that past experiences may not reliably predict future events due to time-dependent variability.

Innovative Approach: MPSE

For the TV-POMDP framework to effectively learn and plan, a two-pronged strategy is introduced: Memory Prioritized State Estimation (MPSE). This method employs a weighted memory approach to enhance estimation accuracy and planning. MPSE assigns weight to observations based on their relevancy and correlation with the current state of the environment, utilizing past data to predict the current transitions more accurately. It then integrates these prioritized samples to optimize state estimation, providing a basis for improved action selection. The model's adaptivity to time variance and partial observability leads to superior performance in navigation tasks.

Performance Validation and Results

The validation of the proposed framework and algorithms was carried out through simulations and hardware experiments involving robots navigating time-varying environments. In scenarios that include an autonomous marine vehicle and a ground vehicle, the results indicated that the new approach outperformed traditional methods, showcasing its potential for real-world applications. This demonstrates that by leveraging thoughtful data prioritization and acknowledging the temporal context, it is possible to achieve more effective decision-making under uncertainty.

Conclusion

The research presented in the paper is groundbreaking in the field of decision-making for autonomous systems facing variable environments. The new TV-POMDP framework, coupled with the MPSE strategy, allows for superior estimation and planning performance. It opens up pathways for realizing autonomous system operations that are resilient and adaptive to the ongoing uncertainties of dynamic conditions, propelling these systems closer to effective real-world deployment.