DARLA: Improving Zero-Shot Transfer in Reinforcement Learning (1707.08475v2)

Abstract: Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target domain. We propose a new multi-stage RL agent, DARLA (DisentAngled Representation Learning Agent), which learns to see before learning to act. DARLA's vision is based on learning a disentangled representation of the observed environment. Once DARLA can see, it is able to acquire source policies that are robust to many domain shifts - even with no access to the target domain. DARLA significantly outperforms conventional baselines in zero-shot domain adaptation scenarios, an effect that holds across a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms (DQN, A3C and EC).

• The paper introduces DARLA, a multi-stage RL agent that employs disentangled representation learning for robust zero-shot transfer across domains.
• Methodology utilizes the β-VAE framework and perceptual similarity losses to build factorized latent encodings that mitigate domain-specific overfitting.
• Empirical evaluations in environments like DeepMind Lab and robotic tasks demonstrate DARLA’s superior sim2real transfer compared to traditional RL methods.

Overview of DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

The paper "DARLA: Improving Zero-Shot Transfer in Reinforcement Learning" introduces an innovative approach to mitigating the longstanding challenge of domain adaptation in deep reinforcement learning (RL). This work proposes DARLA (Disentangled Representation Learning Agent), a multi-stage RL agent designed to efficiently utilize disentangled representation learning for enhancing zero-shot transfer capabilities across domains.

Zero-Shot Transfer and Domain Adaptation

Domain adaptation is a critical problem in RL, especially when acquiring data in target domains is resource-intensive or impractical. Traditional RL methods often fail to generalize in the presence of distributional shifts between source and target domains. DARLA addresses these challenges by employing a disentangled representation learning strategy, thereby enabling agents to learn robust policies that are invariant to domain-specific variations.

Disentangled Representation Learning

At the core of DARLA is the disentangled representation learning which encapsulates environmental factors into a factorized latent representation. This approach diverges from conventional RL methods that often result in entangled latent states, susceptible to overfitting on the source domain. DARLA's approach involves a three-stage process: learning to see (through unsupervised disentangled representation learning), learning to act (using the robust representation on source tasks), and transfer (evaluating zero-shot performance in target domains).

Methodology and Implementation

To implement DARLA, the authors leverage the $\beta$-VAE framework to enforce disentanglement in the latent variables. This is augmented with a perceptual similarity loss, capitalizing on a pre-trained denoising autoencoder. This methodological choice effectively limits the latent space capacity, encouraging factorized encodings aligned with high-level visual features rather than pixel-level statistics.

DARLA's performance was assessed across several RL environments, including DeepMind Lab and robotic control tasks with the Jaco arm in both simulated (MuJoCo) and real-world scenarios. The paper shows that DARLA significantly outperforms traditional and baseline RL algorithms, achieving strong zero-shot transfer performance across a wide array of tasks and base RL algorithms (DQN, A3C, and Episodic Control).

Empirical Results

Quantitative evaluation in zero-shot domain adaptation scenarios demonstrated DARLA's superiority through consistent and substantial improvements over baseline methods. For instance, in sim2real tasks, DARLA's performance is notable due to its ability to mitigate the perceptual-reality gap. Moreover, the paper presents a strong positive correlation between the degree of disentanglement in the representation and the zero-shot transfer performance, which validates the hypothesis that disentangled representations are crucial for robust domain adaptation in RL.

Theoretical and Practical Implications

From a theoretical perspective, this work confirms the potential of disentangled representation learning as a foundational component for generalized policy learning in RL. Practically, DARLA provides a pathway for deploying RL models in environments where acquiring target domain data is challenging, thus broadening the applicability of RL in real-world scenarios such as robotics and autonomous systems.

Future Directions

Future research could explore extending DARLA's approach to asynchronous and adversarial environments, where domain shifts are dynamic and potentially adversarial. Additionally, exploring the combination of DARLA with meta-learning frameworks could further augment the zero-shot transfer capabilities.

Conclusion

DARLA stands as a significant advance in the field of reinforcement learning, offering a robust framework for achieving zero-shot transfer through disentangled representation learning. This paper underscores the importance of representation quality in RL policy transfer, marking a step forward in bridging the gap between simulation and real-world application.