Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error
arXiv:2604.01613v1 Announce Type: new Abstract: In reinforcement learning (RL), temporal difference (TD) errors are widely adopted for optimizing value and policy functions. However, since the TD error is defined by a bootstrap method, its computation tends to be noisy and destabilize learning. Heuristics to improve the accuracy of TD errors, such as target networks and ensemble models, have been introduced so far. While these are essential approaches for the current deep RL algorithms, they cause side effects like increased computational cost and reduced learning efficiency. Therefore, this paper revisits the TD learning algorithm based on control as inference, deriving a novel algorithm capable of robust learning against noisy TD errors. First, the distribution model of optimality, a binary random variable, is represented by a sigmoid function. Alongside forward and reverse Kullback-Leibler divergences, this new model derives a robust learning rule: when the sigmoid function saturat
arXiv:2604.01613v1 Announce Type: new Abstract: In reinforcement learning (RL), temporal difference (TD) errors are widely adopted for optimizing value and policy functions. However, since the TD error is defined by a bootstrap method, its computation tends to be noisy and destabilize learning. Heuristics to improve the accuracy of TD errors, such as target networks and ensemble models, have been introduced so far. While these are essential approaches for the current deep RL algorithms, they cause side effects like increased computational cost and reduced learning efficiency. Therefore, this paper revisits the TD learning algorithm based on control as inference, deriving a novel algorithm capable of robust learning against noisy TD errors. First, the distribution model of optimality, a binary random variable, is represented by a sigmoid function. Alongside forward and reverse Kullback-Leibler divergences, this new model derives a robust learning rule: when the sigmoid function saturates with a large TD error probably due to noise, the gradient vanishes, implicitly excluding it from learning. Furthermore, the two divergences exhibit distinct gradient-vanishing characteristics. Building on these analyses, the optimality is decomposed into multiple levels to achieve pseudo-quantization of TD errors, aiming for further noise reduction. Additionally, a Jensen-Shannon divergence-based approach is approximately derived to inherit the characteristics of both divergences. These benefits are verified through RL benchmarks, demonstrating stable learning even when heuristics are insufficient or rewards contain noise.
Executive Summary
This article proposes a novel reinforcement learning algorithm, pseudo-quantized actor-critic, designed to mitigate the effects of noisy temporal difference errors. By employing a sigmoid function to model the distribution of optimality and incorporating forward and reverse Kullback-Leibler divergences, the algorithm effectively excludes large TD errors caused by noise from the learning process. The proposed method also introduces pseudo-quantization of TD errors by decomposing optimality into multiple levels. The algorithm's efficacy is demonstrated through RL benchmarks, showcasing stable learning even in noisy environments. The pseudo-quantized actor-critic algorithm offers a promising solution for robust reinforcement learning, particularly in scenarios where traditional heuristics are insufficient.
Key Points
- ▸ Pseudo-quantized actor-critic algorithm mitigates noisy temporal difference errors
- ▸ Employment of sigmoid function and Kullback-Leibler divergences for robust learning
- ▸ Pseudo-quantization of TD errors through decomposition of optimality
Merits
Strength in Robustness
The pseudo-quantized actor-critic algorithm effectively excludes large TD errors caused by noise from the learning process, ensuring robust learning in noisy environments.
Improved Learning Efficiency
By leveraging the sigmoid function and Kullback-Leibler divergences, the algorithm enables stable learning without the need for traditional heuristics, resulting in improved learning efficiency.
Demerits
Limited Experimental Scope
The article's experimental evaluation is primarily limited to RL benchmarks, and it remains unclear whether the pseudo-quantized actor-critic algorithm generalizes to other reinforcement learning domains or scenarios.
Expert Commentary
The pseudo-quantized actor-critic algorithm represents a significant advancement in the field of reinforcement learning, as it addresses the critical challenge of robust learning in noisy environments. By leveraging novel mathematical concepts, such as the sigmoid function and Kullback-Leibler divergences, the algorithm effectively excludes large TD errors from the learning process. While the article's experimental evaluation is primarily limited to RL benchmarks, the proposed algorithm's potential for practical and policy-relevant applications is substantial. As reinforcement learning continues to gain traction in real-world scenarios, the pseudo-quantized actor-critic algorithm offers a promising solution for ensuring robust learning in uncertain environments.
Recommendations
- ✓ Future research should focus on extending the pseudo-quantized actor-critic algorithm to other reinforcement learning domains and scenarios.
- ✓ The proposed algorithm should be evaluated in more complex and realistic environments to further assess its robustness and learning efficiency.
Sources
Original: arXiv - cs.LG
