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PvP ML

This contains the core reinforcement learning logic that drives the entire project. It includes scripts for orchestrating the training process, evaluating the environment, and serving models through a socket-based API.

Example Training Run:

Eval Win Rate

How To Use

This requires having conda (or some variant of it) installed, so that the conda command is available.

  1. Create the environment: conda env create -p ./env -f environment.yml.
  2. Activate the environment: conda activate ./env.

For CPU-only training, uncomment cpuonly in the conda environment file before creating the environment. By default, training uses GPU if available.

Evaluate Model (on simulation)

This will run an agent on a simulated server to fight against.

  1. Run the evaluation script with a model: eval --model-path <model-path-here>.
  2. Log in to the simulated server and play against the agent!

Serve Models via API

This serves models in models via a socket-based API for fast predictions.

  1. Start the API: serve-api.
  2. Connect using a client (example: PvpClient).

By default, it only accepts connections on 127.0.0.1, configurable with --host.

Start Training Job

  1. Configure the job in ./config - or use an existing config such as PastSelfPlay.
  2. Start the job: train --preset PastSelfPlay --name <name-your-experiment>.
  3. Stop the job: train cleanup --name <your-experiment-name> or train cleanup --name all to terminate all jobs.

Note: Training logs are stored in ./logs and experiment data, including model versions, are stored in ./experiments.

Tensorboard

  • Tensorboard automatically launches with training jobs, or run train tensorboard to start it manually. Access it at http://127.0.0.1:6006/.
  • Tensorboard logs are stored in ./tensorboard under the experiment name.

Tensorboard Metrics Visualization:

Tensorboard Metrics

Features

  • Generalized PvP environment setup.
  • Model evaluation support.
  • Model serving through a socket-based API.
  • Distributed rollout collection.
  • Parameterized and masked actions, including autoregressive actions (with normalization).
  • TorchScript-compatible models for efficient evaluation.
  • Self-play strategies, including prioritized past-self play (based on OpenAI Five paper).
  • Adversarial training (based on DeepMind's SC2 paper).
  • Reward normalization and observation normalization.
  • Novelty rewards.
  • Distributed model processing via various RemoteProcessor implementations.
  • Noise generation.
  • Flexible parameter annealing through comprehensive scheduling.
  • Asynchronous training job management.
  • Comprehensive metric recording (Tensoboard).
  • Scripted plugins for evaluation and API.
  • PPO implementation.
  • Async vectorized environment.
  • Customizable model architectures.
  • Gradient accumulation.
  • Detailed configuration via YAML.
  • PvP Environment implementation with configurable rewards.
  • Full game state visibility for the critic.
  • Frame stacking.
  • Comprehensive callback system.
  • Environment randomization for generalization.
  • Elo-based ranking and rating generation for benchmarking.
  • Supplementary model for episode outcome prediction.

Distributed Training

  • Supports Ray for distributed rollouts on a cluster or multiple CPU cores.
  • Train with distribution: train --preset <preset> --distribute <parallel-rollout-count>.
  • Omit <parallel-rollout-count> to use all available CPU cores.

Cluster Management (via AWS)

  • Scale up a cluster: ray up cluster.yml.
  • Scale down a cluster: ray down cluster.yml.
  • View the cluster: ray attach cluster.yml --port-forward=8265 to open dashboard.

NH Environment

  • Focuses on 1v1 NH fights.
  • MultiDiscrete action space with 11 action heads.
  • Extensive observation space.

See the environment contract for details.

Pre-Trained Models

  • Available in models.
  • Trained for PvP Arena/LMS for various builds and gear setups.
  • Includes GeneralizedNh (self-play) and FineTunedNh (GeneralizedNh fine-tuned against human approximations).

Possible Enhancements

Better Human Prediction

  • Investigate bootstrapping from human replays for improved human-like behavior.
  • Consider blending behavior cloning with self-play.

Memory

  • Experiment with LSTM or transformer architectures for episode recall and strategy adaptation.

Note: Some experimentation was done with transformers (with frame-stacking), but simple FF networks learned quicker and outperformed the more complex networks.

Fine-Tune Agents On Live Game

  • Explore rollouts on the live game for enhanced realism and human player adaptation.

Helpful Resources

These are some resources that helped the most when working on this project.