[Paper Review] Active Domain Randomization

Bhairav Mehta, Manfred Diaz+3 more|arXiv (Cornell University)|Apr 9, 2019

Domain Adaptation and Few-Shot Learning32 references33 citations

TL;DR

Active Domain Randomization (ADR) learns a parameter-sampling strategy to focus training on the most informative environment variations, improving generalization and robustness over Uniform Domain Randomization (UDR).

ABSTRACT

Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters. We propose Active Domain Randomization, a novel algorithm that learns a parameter sampling strategy. Our method looks for the most informative environment variations within the given randomization ranges by leveraging the discrepancies of policy rollouts in randomized and reference environment instances. We find that training more frequently on these instances leads to better overall agent generalization. Our experiments across various physics-based simulated and real-robot tasks show that this enhancement leads to more robust, consistent policies.

Motivation & Objective

• Investigate why uniform randomization can yield high-variance, suboptimal policies.
• Propose ADR to learn informative environment variations during training.
• Demonstrate ADR's effectiveness across simulated and real-robot tasks and high-dimensional parameter spaces.

Proposed method

• Formulate DR as an RL problem where sampling policies are optimized with Stein Variational Policy Gradient (SVPG).
• Use a discriminator to measure discrepancies between trajectories in a reference versus randomized environments to provide a learning signal.
• Train an ensemble of SVPG particles to propose diverse, informative randomized environments.
• Update the agent policy on environments proposed by the SVPG particles while updating the discriminator to guide sampling.”,

Experimental results

Research questions

• RQ1 Does uniform sampling of randomization parameters lead to suboptimal generalization compared to targeted sampling?
• RQ2 Can ADR improve generalization and reduce policy variance across a range of tasks and dimensionality of randomization space?
• RQ3 Is a discriminator-based reward effective for guiding the sampling policy toward informative environments?
• RQ4 Do policies trained with ADR transfer more robustly to real robots (sim-to-real) than those trained with UDR?

Key findings

• ADR matches or approaches expert-level generalization in hard settings where UDR fails.
• ADR produces policies with lower variance and more consistent performance across environments.
• ADR scales to high-dimensional randomization spaces and improves sim-to-real transfer without requiring target-domain rewards.
• Training on problematic environments more frequently yields better overall generalization than uniform sampling.
• ADR provides interpretability by highlighting which environment regions are hard and need more training.”],
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• title
• Active Domain Randomization
• tldr
• Active Domain Randomization (ADR) learns informative environment variations via SVPG-guided sampling, improving generalization and robustness versus Uniform DR.
• meta_description
• Improve zero-shot transfer with Active Domain Randomization (ADR): learn where to sample simulation parameters to boost policy generalization across domains and in sim-to-real.
• objective
• - Investigate why uniform randomization can yield high-variance, suboptimal policies.
• - Propose ADR to learn informative environment variations during training.
• - Demonstrate ADR's effectiveness across simulated and real-robot tasks and high-dimensional parameter spaces.
• method
• - Formulate DR as an RL problem where sampling policies are optimized with Stein Variational Policy Gradient (SVPG).
• - Use a discriminator to measure discrepancies between trajectories in a reference versus randomized environments to provide a learning signal.
• - Train an ensemble of SVPG particles to propose diverse, informative randomized environments.
• - Update the agent policy on environments proposed by the SVPG particles while updating the discriminator to guide sampling.
• - Apply ADR across multiple environments and show improved generalization and robustness.
• research_questions
• - Does uniform sampling of randomization parameters lead to suboptimal generalization compared to targeted sampling?
• - Can ADR improve generalization and reduce policy variance across a range of tasks and dimensionality of randomization space?
• - Is a discriminator-based reward effective for guiding the sampling policy toward informative environments?
• - Do policies trained with ADR transfer more robustly to real robots (sim-to-real) than those trained with UDR?
• key_findings
• - ADR matches or approaches expert-level generalization in hard settings where UDR fails.
• - ADR produces policies with lower variance and more consistent performance across environments.
• - ADR scales to high-dimensional randomization spaces and improves sim-to-real transfer without requiring target-domain rewards.
• - Training on problematic environments more frequently yields better overall generalization than uniform sampling.
• - ADR provides interpretability by highlighting which environment regions are hard and need more training.
• table_headers
• []
• table_rows
• []