Core Concepts

This work proposes a simple imitation learning algorithm, BC-MAX, that learns a policy by imitating the best baseline policy for each context in a dataset collected from multiple suboptimal baseline policies. The authors provide a sample complexity bound on the accuracy of the learned policy and show that the algorithm is minimax optimal.

Abstract

The authors study a reinforcement learning (RL) problem where they are given a set of trajectories collected using K baseline policies, each of which can be quite suboptimal in isolation but have strong performance in complementary parts of the state space. The goal is to learn a policy that performs as well as the best combination of baselines on the entire state space.
The key contributions are:
The authors propose a simple imitation learning algorithm, BC-MAX, that combines the multiple baseline policies by executing each policy in every starting state in the dataset and then imitating the trajectory of the policy with the highest reward in that state.
They provide an upper bound on the expected regret of the learned policy to the maximal reward obtained in each starting state by choosing the best baseline policy for that state.
They complement the analysis with a lower bound showing that the result is unimprovable beyond polylogarithmic factors in their setting.
They apply BC-MAX to two real-world datasets for the task of optimizing compiler inlining for binary size, and show that they outperform strong baselines in both cases.

Stats

The authors assume that the rewards are bounded and non-negative, that is r(τ) ∈ [0, B] for all trajectories τ and some constant B.

Quotes

"We are particularly interested in challenging settings where the underlying RL problem has a long horizon, and we only receive sparse trajectory-level feedback at the end of each trajectory."
"We demonstrate the versatility of BC-MAX by iteratively applying BC-MAX on the initial expert, along with all prior policies trained using previous BC-MAX iterations as the next set of baselines."

Key Insights Distilled From

by Teodor V. Ma... at **arxiv.org** 03-29-2024

Deeper Inquiries

To handle cases where the baseline policies have different strengths in different parts of the state space, the algorithm can be extended by incorporating a mechanism to adaptively combine the strengths of the baseline policies based on the observed performance in each region of the state space. This adaptation can be achieved by introducing a weighting mechanism that dynamically adjusts the influence of each baseline policy based on its performance in specific regions. By allowing the algorithm to learn and update these weights over time, it can effectively leverage the diverse strengths of the baseline policies across the entire state space. Additionally, the algorithm can incorporate a mechanism for exploration that focuses on regions where the baseline policies exhibit varying strengths, allowing for a more comprehensive learning process.

The assumption that transitions and rewards are deterministic can be limiting in real-world scenarios where stochasticity plays a significant role. To adapt the algorithm to handle stochastic environments, several modifications can be made. One approach is to incorporate probabilistic models for transitions and rewards, allowing the algorithm to account for uncertainty in the environment. This can involve using techniques such as Monte Carlo methods or Bayesian inference to estimate transition probabilities and reward distributions. Additionally, the algorithm can be modified to incorporate exploration strategies that explicitly account for uncertainty, such as Thompson sampling or epsilon-greedy policies. By adapting the algorithm to handle stochastic environments, it can better capture the complexities and uncertainties present in real-world scenarios.

The insights from leveraging multiple baseline policies in reinforcement learning can be applied to other areas of machine learning beyond reinforcement learning, such as supervised learning or unsupervised learning. In supervised learning, the concept of leveraging multiple baseline models with complementary strengths can be applied in ensemble learning techniques, where diverse models are combined to improve predictive performance. This can include techniques like bagging, boosting, or stacking, which leverage the strengths of different models to enhance overall performance. In unsupervised learning, the idea of combining multiple baseline approaches can be applied in clustering or dimensionality reduction tasks to improve the robustness and accuracy of the learned representations. By incorporating the principles of leveraging diverse baseline models, machine learning algorithms in various domains can benefit from improved generalization and performance.

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