Efficient q-learning method improves robustness in reinforcement learning
Provably Efficient Q-learning for Robust Reinforcement Learning
Artificial IntelligenceMachine Learning
Summary
Reinforcement learning algorithms often struggle when conditions change unexpectedly, like when a robot trained in simulation moves to the real world. The authors propose a new Q-learning method that learns reliably even when there is uncertainty in the environment. Their method works by considering a range of possible changes and still finding good decision strategies efficiently. This approach helps ensure that the learned behaviors remain effective despite unpredictable differences between training and deployment environments.
What this means in practice
- •For robotics engineers: Build control systems that continue to perform reliably when robots face unexpected environmental changes after training in simulation.
- •For autonomous vehicle developers: Improve decision-making algorithms to handle unforeseen conditions encountered when shifting from simulation-based training to real-world driving scenarios.
Authors
Yuchen Jiao, Gen Li
Abstract
Standard reinforcement learning (RL) algorithms based on Markov Decision Processes (MDPs) are vulnerable to sim-to-real gaps. To address this issue, distributionally robust RL algorithms based on robust MDPs (RMDPs) have been proposed, for ensuring that agents can perform reliably to unpredictable conditions in real-world environments. Despite of the rich literature, \revision{an efficient model-free method is still lacking} for robust RL, particularly for Markovian data, which is more relevant in practical applications. In this work, we take a step towards settling this problem by proposing a sample efficient (asynchronous Q-learning) algorithm for robust RL. In particular, consider a $γ$-discounted infinite-horizon RMDP with state space $\mathcal{S}$ and action space $\mathcal{A}$, where the uncertainty set is specified by the total variation (TV) distance with uncertainty level $σ$. Assuming access to a single trajectory of Markovian samples induced by a behavior policy and the nominal transition kernel, the sample complexity of the proposed Q-learning algorithm --- to achieve an entrywise $\varepsilon$-approximation of the optimal Q-function --- is bounded by $$ \frac{1}{μ_{\mathsf{min}}\varepsilon^2(1-γ)^3\max\{1-γ,σ\}} + \frac{t_{\mathsf{mix}}}{μ_{\mathsf{min}}(1-γ)} $$ up to logarithmic factors. Here, $t_{\mathsf{mix}}$ and $μ_{\mathsf{min}}$ denote the mixing time and the minimum state-action occupancy probability of the sample trajectory, respectively. This result is nearly minimax optimal, with an additional factor of $\frac{1}{1-γ}$. Moreover, it achieves a significant improvement over existing bounds.