REVIEW 6 cited by
AndroidEnv: A Reinforcement Learning Platform for Android
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv allows RL agents to interact with a wide variety of apps and services commonly used by humans through a universal touchscreen interface. Since agents train on a realistic simulation of an Android device, they have the potential to be deployed on real devices. In this report, we give an overview of the environment, highlighting the significant features it provides for research, and we present an empirical evaluation of some popular reinforcement learning agents on a set of tasks built on this platform.
Forward citations
Cited by 6 Pith papers
-
ASPERA: A Simulated Environment to Evaluate Planning for Complex Action Execution
ASPERA generates a benchmark of 250 executable assistant tasks and finds that LLMs, even with full API documentation, solve only 10 to 80 percent of them.
-
MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation
A memory-augmented LLM planner that stores and retrieves page-level summaries from past trajectories improves success rates on mobile GUI task benchmarks.
-
Mirage-1: Augmenting and Updating GUI Agent with Hierarchical Multimodal Skills
Mirage-1 combines a hierarchical multimodal skill memory with a skill-augmented Monte Carlo tree search to outperform prior GUI agents on Android and web online benchmarks.
-
X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System
X-WebAgentBench translates the WebShop e-commerce agent task into 14 languages and shows that current agents, including GPT-4o, perform substantially worse in multilingual settings than in English.
-
Advancing Autonomous VLM Agents via Variational Subgoal-Conditioned Reinforcement Learning
VSC-RL combines VLM-generated subgoals with a subgoal-conditioned AWR-style RL objective and claims improved sample efficiency over DigiRL and WebRL on AitW and WebArena-Lite.
-
AppVLM: A Lightweight Vision Language Model for Online App Control
A 3B VLM fine-tuned with SFT on AndroidControl and ReST-style iterations on AndroidWorld achieves competitive AndroidWorld success rates with GPT-4o while running about ten times faster.
Discussion (0). Continue with ORCID to comment.