λ-Models: Decision-Aware Reinforcement Learning Study

Grunnleggende konsepter

The study explores decision-aware model learning in reinforcement learning, emphasizing the importance of latent models and value-aware losses for improved performance.

Sammendrag

The study investigates decision-aware model learning in reinforcement learning, focusing on the significance of latent models and value-aware losses. It compares IterVAML and MuZero algorithms, highlighting their theoretical and practical differences. The research delves into the impact of stochastic environments on these algorithms, providing empirical evidence to support the findings. Additionally, it evaluates the performance of different loss functions in challenging environments like Humanoid-run. The study concludes by recommending decision-aware approaches for complex environments where traditional modeling methods fall short.

Statistikk

"In this paper, we present an overview of which decision-aware loss functions are best used in what empirical scenarios."
"We show that IterVAML is a stable loss when a latent model is used."
"The MuZero value function learning scheme results in a bias in stochastic environments."

Sitater

"We highlight that empirical design decisions established in the MuZero line of works are vital to achieving good performance for related algorithms."
"Decision-aware losses can be insufficient for stable learning, especially in continuous state-action spaces."
"The bias leads to a quantifiable difference in performance."

Viktige innsikter hentet fra

$λ$-models

by Claas A Voel... klokken arxiv.org 03-04-2024

https://arxiv.org/pdf/2306.17366.pdf

Dypere Spørsmål

When do decision-aware models show performance improvements over the simple BYOL loss

Decision-aware models show performance improvements over the simple BYOL loss when the complexity of the environment makes it challenging to accurately model all aspects. In scenarios where learning a precise model is infeasible due to limited samples or model capacity, decision-aware losses can provide benefits. These losses focus on finding models that provide accurate value function estimates rather than just maximizing likelihood estimation. By incorporating decision-awareness into the learning process, algorithms like IterVAML and MuZero can make predictions that result in correct value estimation, leading to improved performance in complex environments.

Can decision-aware models be used for both value function learning and policy improvement

Yes, decision-aware models can be used for both value function learning and policy improvement. By leveraging a combination of latent spaces and decision-aware loss functions, these models can effectively learn representations that enhance both value function estimation and policy gradient computation. The use of latent spaces allows for more stable learning by reducing sharp gradients often encountered with state-space prediction models. Additionally, employing stabilizing losses like latent self-prediction further enhances the quality of learned representations for better policy gradient estimation.

How can biases in value function estimates be mitigated in stochastic environments

Biases in value function estimates in stochastic environments can be mitigated through careful algorithm design and implementation choices. For example:

IterVAML: Restricting IterVAML to deterministic models ensures unbiased solutions even with stochastic transitions under certain conditions.
MuZero: Addressing biases introduced by MuZero's joint model- and value-function learning algorithm requires attention to variance levels impacting the solution.
By understanding these biases and their implications on performance, researchers can develop strategies such as using real rewards for better target estimations or exploring alternative approaches like bootstrap estimates tailored for specific environments' characteristics. Through theoretical analysis paired with empirical validation in stochastic settings like humanoid-run tasks from benchmark suites, researchers gain insights into effective ways to mitigate biases in value function estimates within decision-aware reinforcement learning frameworks.