Navigating the Noisy Neighborhoods of Continuous Control Policy Landscapes

Konsep Inti

Deep reinforcement learning agents in continuous control tasks exhibit significant instability in their performance, with a single update to the policy parameters leading to a wide range of returns. By studying the distribution of returns in the neighborhood of a policy, we reveal the existence of "noisy neighborhoods" in the return landscape, where policies of similar average return can have vastly different stability profiles. We show that these unstable policies are prone to sudden failures, and develop a procedure to navigate the landscape and find more robust policies.

Abstrak

The paper investigates the return landscape in continuous control tasks, as traversed by deep reinforcement learning algorithms. The authors demonstrate the existence of "noisy neighborhoods" in the return landscape, where a single update to the policy parameters can lead to a wide range of returns.
Key insights:

The authors take a distributional view on the return landscape by studying the post-update return distribution, which captures the variability of returns in the neighborhood of a policy.
They find that policies with similar average returns can have vastly different post-update return distributions, corresponding to qualitatively different agent behaviors.
The authors analyze the mechanism behind the sudden failures observed in the unstable policies, showing that these policies are on the edge of catastrophic collapse.
By studying the global structure of the return landscape through linear interpolation between policies, the authors find that policies from the same training run are connected by smooth paths with no valleys of low return.
The authors develop a procedure that leverages the post-update return distribution to navigate the landscape and find more robust policies.
Overall, the paper provides new insights into the optimization, evaluation, and design of deep reinforcement learning agents for continuous control tasks.

Statistik

"Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time."
"We find that popular algorithms traverse noisy neighborhoods of this landscape, in which a single update to the policy parameters leads to a wide range of returns."
"We discover that many of these distributions are long-tailed and we find the source of these tails to be sudden failures from an otherwise successful policy."
"Surprisingly, while large valleys of low return are visible when linearly interpolating between similarly performing policies from different runs, we show no such valleys typically exist between policies from the same run."

Kutipan

"We call the return landscape the mapping from θ to R(θ), our main object of study. We show that the return often varies substantially within the vicinity of any given θ, forming what we call a noisy neighborhood of θ."
"We believe the phenomenon we study is central to deep reinforcement learning in continuous control, a finding which parallels a sensitive dependence on initial conditions observed previously in the Cartpole domain."
"Our results suggest that some of the previously-observed reliability issues in deep reinforcement learning agents for continuous control may be due to the fundamental structure of the return landscape for neural network policies."

Wawasan Utama Disaring Dari

Policy Optimization in a Noisy Neighborhood

by Nate Rahn,Pi... pada arxiv.org 04-12-2024

https://arxiv.org/pdf/2309.14597.pdf

Pertanyaan yang Lebih Dalam

How do the characteristics of the return landscape vary across different continuous control environments and task complexities

The characteristics of the return landscape can vary significantly across different continuous control environments and task complexities. In the study mentioned, the researchers focused on environments modeled as finite-horizon Markov Decision Processes (MDPs) in continuous control tasks. They found that the return landscape exhibited high-frequency discontinuities, leading to significant variations in performance over time. These variations made it challenging to compare different algorithms or implementations, as well as to measure an agent's progress reliably from episode to episode.
In more complex tasks or environments, such as those with higher-dimensional state and action spaces or more intricate dynamics, the return landscape may exhibit even more pronounced variations. The researchers observed that policies produced by popular deep RL algorithms traversed noisy neighborhoods in the landscape, where a single update to the policy parameters could lead to a wide range of returns. This instability in the return landscape could be influenced by factors such as the complexity of the environment, the stochasticity in the updates, and the non-linearities in the policy parameter space.

Can the insights from this work be leveraged to design more robust deep RL algorithms that can reliably navigate the return landscape

The insights from this work can indeed be leveraged to design more robust deep RL algorithms that can reliably navigate the return landscape. By studying the distribution of returns in the neighborhood of policies, researchers were able to characterize failure-prone regions of policy space and identify hidden dimensions of policy quality. They found that different policies with similar mean returns could exhibit significantly different distributional profiles, indicating diverse behaviors and levels of stability.
One approach to designing more robust algorithms could involve incorporating a distribution-aware procedure that identifies and avoids noisy neighborhoods in the return landscape. By rejecting gradient updates that lead to policies with less favorable post-update return distributions, algorithms can potentially stabilize policies and improve their robustness to perturbations. This rejection mechanism, as demonstrated in the study, can effectively reduce the left-tail probability of policies, making them less prone to sudden failures and performance drops.

What are the implications of the discovered "noisy neighborhoods" and "smooth paths" in the return landscape for the generalization and transfer of deep RL policies

The discovered "noisy neighborhoods" and "smooth paths" in the return landscape have important implications for the generalization and transfer of deep RL policies. Noisy neighborhoods, where small updates to policy parameters result in a wide range of returns, can indicate regions of instability and unpredictability in the policy space. Policies residing in these neighborhoods may exhibit erratic behaviors and be more susceptible to performance fluctuations.
On the other hand, smooth paths in the return landscape, where policies from the same run are connected by linear paths with no valleys of low return, suggest regions of stability and consistency in policy performance. Policies along these paths may generalize better to unseen scenarios and transfer more effectively to new environments. By navigating towards smoother regions and avoiding noisy neighborhoods, deep RL algorithms can potentially improve the generalization and transfer capabilities of learned policies, making them more reliable and adaptable in real-world applications.