✨ TL;DR
Platypoos is a planning algorithm for deterministic environments with stochastic rewards that automatically adapts to unknown reward scales and smoothness without requiring prior knowledge of discount factors or reward bounds. It achieves optimal sample complexity with matching upper and lower bounds.
Planning in reinforcement learning environments with deterministic dynamics and stochastic rewards presents challenges when the reward function's scale and smoothness are unknown. Traditional planning algorithms often require knowledge of reward bounds or discount factors in advance, limiting their practical applicability. When rewards are unbounded and their scale varies significantly, existing methods may fail to adapt efficiently, leading to suboptimal sample complexity. The problem is further complicated by the need to handle discounted returns across different discount factors simultaneously. Without knowing the appropriate scale of the reward function or the effective planning horizon determined by the discount factor, algorithms must either make conservative assumptions that hurt performance or require manual tuning for each problem instance.
Platypoos is a scale-free adaptive planning algorithm designed to work without prior knowledge of reward scales, smoothness parameters, or discount factors. The algorithm automatically adapts to the unknown characteristics of the reward function during planning. The key innovation is its ability to operate across a broad range of discount factors and reward scales simultaneously. The algorithm employs adaptive techniques that allow it to discover the relevant scale and smoothness of rewards through sampling, rather than requiring these as input parameters. This scale-free property means the algorithm's performance guarantees hold uniformly across different problem instances without manual parameter tuning. The method is specifically designed for deterministic dynamics, leveraging this structure to achieve improved sample complexity.
What the paper shows.
Platypoos achieves improved sample complexity compared to prior work in planning with deterministic dynamics and discounted rewards. The algorithm's performance guarantees hold simultaneously over a broad range of discount factors and reward scales without requiring the algorithm to know these parameters in advance. The authors establish both an upper bound on sample complexity through their algorithm's analysis and a matching lower bound, proving that Platypoos is optimal up to constant factors. This optimality result demonstrates that no algorithm can fundamentally improve upon Platypoos's sample complexity for this problem setting.
The algorithm is specifically designed for deterministic dynamics, which limits its applicability to stochastic transition environments. While the paper addresses unbounded rewards, the practical performance may depend on the actual reward distribution characteristics encountered. The analysis provides asymptotic guarantees that are optimal up to constants, but the constant factors in the sample complexity bounds are not explicitly characterized, which could matter in finite-sample regimes. The paper does not provide empirical evaluations or comparisons with existing methods, leaving practical performance questions unanswered. Additionally, the focus on discounted rewards means the results may not directly extend to other return formulations like average reward or finite-horizon settings.
✨ Generated by Claude · Apr 21, 2026 · Read the PDF for authoritative content.