Leveraging Variance Reduction and Experience Replay to Accelerate Policy Optimization in Reinforcement Learning

Kernekoncepter

The core message of this paper is to propose a novel Variance Reduction Experience Replay (VRER) framework that can intelligently select and reuse the most relevant historical samples to improve the sample efficiency and estimation accuracy of policy gradient in reinforcement learning.

Resumé

The paper addresses the challenge of low sample efficiency in reinforcement learning (RL), especially for complex stochastic systems. It proposes a novel Variance Reduction Experience Replay (VRER) framework that can selectively reuse historical samples to accelerate the learning of optimal policies.
The key highlights are:

The VRER framework can seamlessly integrate with different policy optimization algorithms to improve their sample efficiency. It adaptively selects the most relevant historical samples to reduce the variance of policy gradient estimation.

The paper introduces a novel theoretical framework to analyze the bias-variance trade-off in policy gradient estimation when reusing historical samples. This framework accounts for the impact of Markovian noise, behavior policy interdependencies, and buffer size on the convergence of ER-based RL algorithms.

The theoretical analysis reveals that replaying older samples introduces a larger bias, while reusing more historical samples reduces the gradient estimation variance. This bias-variance trade-off directly influences the convergence rate of ER-based policy optimization.

Building on the theoretical insights, the paper proposes an efficient approximate selection rule to identify the most relevant historical samples and develop the Policy Gradient with VRER (PG-VRER) algorithm.

Extensive experiments demonstrate that the VRER framework can consistently accelerate the learning of optimal policies compared to state-of-the-art policy optimization approaches.

Statistik

The absolute value of the reward function r(s, a) is bounded by a constant Ur.
The policy function πθ(a|s) is Lipschitz continuous and has bounded likelihood ratio with respect to the policy parameter θ.
The Markov decision process (MDP) is uniformly ergodic, with a decreasing function φ(t) that bounds the total variation distance between the t-step state transition distribution and the stationary distribution.

Citater

"For reinforcement learning on complex stochastic systems, it is desirable to effectively leverage the information from historical samples collected in previous iterations to accelerate policy optimization."
"The lack of a rigorous understanding of the experience replay approach in the literature motivates us to introduce a novel theoretical framework that accounts for sample dependencies induced by Markovian noise and behavior policy interdependencies."
"This theoretical framework reveals a crucial bias-variance trade-off in policy gradient estimation: the reuse of older experience tends to introduce a larger bias while simultaneously reducing gradient estimation variance."

Vigtigste indsigter udtrukket fra

Variance Reduction based Experience Replay for Policy Optimization

by Hua Zheng,We... kl. arxiv.org 04-16-2024

https://arxiv.org/pdf/2110.08902.pdf

Dybere Forespørgsler

How can the proposed VRER framework be extended to handle partial observability or multi-agent settings in reinforcement learning

The proposed Variance Reduction Experience Replay (VRER) framework can be extended to handle partial observability or multi-agent settings in reinforcement learning by incorporating techniques such as Partially Observable Markov Decision Processes (POMDPs) and Multi-Agent Reinforcement Learning (MARL).
For handling partial observability, VRER can integrate observation histories or memory states to capture the partial information available to the agent. By incorporating techniques like Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks, VRER can effectively model the temporal dependencies in the observations and make informed decisions in partially observable environments.
In the case of multi-agent settings, VRER can be extended to consider the interactions and dependencies between multiple agents. By incorporating techniques like centralized training with decentralized execution (CTDE) or multi-agent actor-critic frameworks, VRER can optimize policies for multiple agents simultaneously, taking into account the impact of each agent's actions on the overall system performance.

What are the potential limitations of the variance reduction approach, and how can it be further improved to handle more complex environments or tasks

One potential limitation of the variance reduction approach in VRER is the trade-off between bias and variance in policy gradient estimation. While reducing variance is crucial for stable and efficient learning, it may come at the cost of introducing bias into the gradient estimates. To address this limitation and further improve VRER for handling more complex environments or tasks, several strategies can be considered:

Adaptive Sampling: Implement adaptive sampling techniques that dynamically adjust the sampling strategy based on the uncertainty in the environment. This can help prioritize the selection of samples that are most informative for reducing variance without introducing significant bias.

Importance Weighting: Explore advanced importance weighting techniques that can effectively balance the bias-variance trade-off. Techniques like doubly robust estimators or target policy correction can help mitigate bias while reducing variance in policy gradient estimation.

Exploration Strategies: Incorporate exploration strategies that encourage the agent to explore diverse regions of the state space, leading to a more comprehensive and informative sample collection. Techniques like intrinsic motivation or curiosity-driven exploration can enhance the quality of historical samples used in VRER.

Model-Based Approaches: Integrate model-based reinforcement learning techniques to leverage the learned dynamics model for generating synthetic samples. By combining model-based planning with variance reduction in VRER, the agent can learn more efficiently in complex environments with sparse rewards or high-dimensional state spaces.

Can the insights from the bias-variance trade-off analysis be leveraged to develop adaptive experience replay strategies that dynamically adjust the reuse of historical samples based on the current stage of the learning process

The insights from the bias-variance trade-off analysis can be leveraged to develop adaptive experience replay strategies that dynamically adjust the reuse of historical samples based on the current stage of the learning process. Here are some ways to achieve this:

Bias-Variance Monitoring: Implement a monitoring mechanism that tracks the bias and variance of policy gradient estimates during training. Based on the trade-off analysis, the system can dynamically adjust the reuse of historical samples to balance bias and variance, ensuring stable and efficient learning.

Sample Prioritization: Develop a prioritization scheme that assigns weights to historical samples based on their relevance and impact on policy optimization. By prioritizing samples with lower bias and variance, the agent can focus on learning from more informative experiences, leading to faster convergence and improved performance.

Adaptive Replay Buffer: Design an adaptive replay buffer that dynamically adjusts its capacity and selection criteria based on the learning progress. By removing outdated or less relevant samples and prioritizing recent and informative experiences, the agent can maintain a balance between exploration and exploitation while reducing bias in policy gradient estimation.

Online Learning Strategies: Explore online learning strategies that continuously update the policy based on incoming data and adapt the replay mechanism in real-time. By incorporating online updates and adaptive experience replay, the agent can effectively navigate changing environments and tasks, optimizing policy learning efficiency.