Anti-Poaching Game

This is the environment for the Anti-Poaching game. This is a muli-agent competitive game between poachers and rangers. This is currently on v2, and implements the model as described in the formal model description.

Environment (PettingZoo)

The main environment is implemented in anti_poaching_game.py, following the PettingZoo specification. Utils that the environment needs are placed in the utils folder. This includes the following files:

• game_utils.py includes the Trap and GridState classes used to represent the Conservation Game world internally by ConservationGame.
• wrappers.py includes the NonCategoricalFlatten: ParallelEnv -> ObservationWrapper(ParallelEnv) and StackerWrapper: ParallelEnv -> MultiAgentEnv wrappers. MultiAgentEnv is the RLlib equivalent of ParallelEnv.
• flatten_utils.py contains the box_flatten and box_flatten_obs functions that are used by the wrappers.

Virtual Environment (Python)

To have a ready-to-go environment created for you, execute the following line of code

$ source init.sh

Running tests

Tests can be run from the root folder using pytest as follows:

$ pytest [test/]

Furthermore, tools like coverage can be used to generate more extensive data like branch coverage as followS:

$ coverage --branch -m pytest
$ coverage html 

Examples + RLlib

A few examples are found in the examples folder.

Manual policies

• the fixed_policy.py and the random_policy.py show how to use the game using hand-coded policies, or just to show the basic RL loop.

Rllib examples

The examples run MARL algorithms (Policy Gradients and PPO) on the developed model using RLlib. All code is now implemented in the central script main.py. This runs an RLlib algorithm in Multi-Agent Independent Learning mode for an AntiPoachingGame instance. All examples have parameters that can be specified via command line (use --help to see all options); everything is wrapped to provide compatibility with RLlib.

# from the repository root
$ cd examples/rllib_examples
$ python main.py --grid 5