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Mobile-env

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

mobile-env is an open platform for modeling and simulating wireless mobile networks for reinforcement-learning research.

mobile-env models wireless mobile networks as reinforcement-learning environments, where user equipment moves among base stations and policies coordinate connections to maximize global quality of experience. section 12.4

The environment captures practical coordination trade-offs:

  • Users may connect to multiple base stations for higher data rates, while shared base-station resources create competition. section 12.4
  • Connection quality depends on channel conditions such as signal-to-noise ratio, while quality of experience need not increase linearly with data rate. section 12.4
  • The platform supports centralized and multi-agent policies. section 12.4

Train and run PPO

Create the three-base-station, five-user mobile-small-central-v0 environment with Gymnasium, train Stable Baselines3 PPO for 30,000 steps, then predict actions while rendering each episode. For multi-agent coordination, wrap mobile-small-ma-v0 for RLlib, share one policy across agents, and restore the best checkpoint.

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Demonstrates environment creation, PPO training, rollout rendering, multi-agent wrapping, shared policies, and checkpoint restoration.
Examples
Uses small centralized and multi-agent environments with three base stations, five users, PPO training, TensorBoard logging, and rendered episodes.
What it assumes
Assumes reinforcement-learning basics while demonstrating the environment, training loop, rendering, wrappers, and policy configuration.
What you can do after
Builds and evaluates centralized or multi-agent PPO coordination policies in simulated wireless networks.
How it connects
Connects reinforcement learning, PPO, multi-agent reinforcement learning, base stations, user equipment, data rates, and quality of experience.
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Related concepts

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