Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
(2401.01841)Abstract
A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Such problems are traditionally modeled as non-stationary Markov decision processes (NSMDP). However, existing approaches for decision-making in NSMDPs have two major shortcomings: first, they assume that the updated environmental dynamics at the current time are known (although future dynamics can change); and second, planning is largely pessimistic, i.e., the agent acts safely'' to account for the non-stationary evolution of the environment. We argue that both these assumptions are invalid in practice -- updated environmental conditions are rarely known, and as the agent interacts with the environment, it can learn about the updated dynamics and avoid being pessimistic, at least in states whose dynamics it is confident about. We present a heuristic search algorithm called \textit{Adaptive Monte Carlo Tree Search (ADA-MCTS)} that addresses these challenges. We show that the agent can learn the updated dynamics of the environment over time and then act as it learns, i.e., if the agent is in a region of the state space about which it has updated knowledge, it can avoid being pessimistic. To quantifyupdated knowledge,'' we disintegrate the aleatoric and epistemic uncertainty in the agent's updated belief and show how the agent can use these estimates for decision-making. We compare the proposed approach with the multiple state-of-the-art approaches in decision-making across multiple well-established open-source problems and empirically show that our approach is faster and highly adaptive without sacrificing safety.
Overview
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Agents face challenges in dynamic environments, which change unpredictably and require concurrent learning and decision-making within non-stationary Markov decision processes (NSMDPs).
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The paper introduces Adaptive Monte Carlo Tree Search (ADA-MCTS), which enables agents to learn and adapt to changing environmental dynamics without being uniformly conservative or requiring full knowledge of current conditions.
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ADA-MCTS can distinguish between epistemic and aleatoric uncertainty to adjust the agent's decision-making strategy between risk-averse and reward-seeking behaviors.
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Through extensive evaluation, ADA-MCTS outperformed state-of-the-art methods in various open-source environments, even when these methods had access to more information.
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An ablation study highlighted critical components of ADA-MCTS, showing that a balance between exploration and knowledge transfer is essential for its success.
Overview
In sequential decision-making, agents must often navigate complex environments that can change unpredictably over time. A challenge posed by such dynamic contexts is not only how an agent can learn about these changes but also how it can make decisions concurrently. This paper addresses this challenge within the framework of non-stationary Markov decision processes (NSMDPs), specifically overcoming the limitations of existing methods that either assume current environmental conditions are known or adopt a uniformly conservative approach to uncertainty.
Ada-MCTS: A Heuristic Approach
The authors introduce Adaptive Monte Carlo Tree Search (ADA-MCTS), which escalates the capability of agents to learn and adapt to updated environmental dynamics. Unlike traditional approaches, ADA-MCTS does not operate under the assumption of fully known current conditions or maintain a consistently risk-averse positioning. As the agent gains more knowledge about parts of the state space through interaction, ADA-MCTS empowers the agent to make informed decisions tailored to varying levels of uncertainty.
To discern between uncertainties attributable to lack of data (epistemic) and those inherent to the stochasticity of the environment (aleatoric), the algorithm distinguishes and leverages these two types of uncertainty. This facilitation allows for a dynamic adjustment of the agent's decision-making strategy, switching between risk-averse and reward-seeking behaviors when appropriate.
Experimental Validation
ADA-MCTS was extensively evaluated within several well-established open-source environments, showcasing its ability to outperform state-of-the-art methods. The algorithm proved superior to competitive baselines, especially notable since these baselines were at times provided with more information than ADA-MCTS, specifically access to the true dynamics of the environment.
Could the success of ADA-MCTS be ascribable to particular components of the algorithm? An ablation study confirmed this hypothesis, revealing the pivotal role played by the careful interplay between risk-averse exploration and knowledge transfer from preceding models.
Conclusion
The empirical findings demonstrate ADA-MCTS as a robust algorithm for sequential decision-making in uncertain environments. Its adaptability makes it a valuable tool, not just for theoretical studies, but with practical applications ranging from autonomous driving to resource management. By adjusting its strategy according to the level of knowledge about the environment, ADA-MCTS pushes the envelope for artificial intelligence systems operating in real-world scenarios where change is the only constant.
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