init

README.md

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+# Learning Sturctured Commnunication
+
+A Tensorflow implementation of `LSC`.
+
+
+## Code structure
+
+- `./graph_nets`: contains code for establishing communication sturcture.
+
+- `./examples/`: contains scenarios for Ising Model and Battle Game (also models).
+
+- `train_battle.py`: contains code for training Battle Game models
+
+## Requirements Installation
+```shell
+pip install ./
+```
+## Compile MAgent platform and run
+
+Before running Battle Game environment, you need to compile it. You can get more helps from: [MAgent](https://github.com/geek-ai/MAgent)
+
+**Steps for compiling**
+
+```shell
+cd examples/battle_model
+./build.sh
+```
+
+**Steps for training models under Battle Game settings**
+
+1. Add python path in your `~/.bashrc` or `~/.zshrc`:
+
+    ```shell
+    vim ~/.zshrc
+    export PYTHONPATH=./examples/battle_model/python:${PYTHONPATH}
+    source ~/.zshrc
+    ```
+
+2. Run training script for training:
+
+    ```shell
+    ./runtiny.sh
+    ```

examples/battle_model/.gitignore

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+build/
+**__pycache__

examples/battle_model/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.0)
+
+project(magent)
+
+IF (APPLE)
+    set(CMAKE_CXX_COMPILER "/usr/local/opt/llvm/bin/clang++")
+    link_directories("/usr/local/opt/llvm/lib")
+    include_directories( "/usr/local/include" )
+    link_directories("/usr/local/lib/")
+ENDIF()
+
+file(GLOB autopilot_sources src/*.cc src/gridworld/*.cc src/discrete_snake/*.cc src/utility/*.cc)
+set(LIB_SRC_FILES ${autopilot_sources})
+
+file(GLOB autopilot_sources src/render/*.cc src/render/*/*.cc src/render/*/*/*.cc)
+set(RENDER_SRC_FILES ${autopilot_sources})
+
+set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -Wall -std=c++11 -O3")
+set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-variable -Wno-reorder -Wno-sign-compare -Wno-missing-braces")
+set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp -pthread -DDEBUG")
+
+# runtime library
+add_library(magent SHARED ${LIB_SRC_FILES})
+add_executable(testlib ${LIB_SRC_FILES})
+
+# render
+add_executable(render ${RENDER_SRC_FILES})
+
+IF (APPLE)
+    target_link_libraries(render boost_system jsoncpp argp omp)
+ELSE()
+    target_link_libraries(render boost_system jsoncpp)
+ENDIF()
+
+set_target_properties( render
+        PROPERTIES
+        RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/render/")
+
+# copy frontend and demo
+add_custom_command(
+        TARGET render POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_directory "${CMAKE_SOURCE_DIR}/src/render/frontend/" "${CMAKE_BINARY_DIR}/render"
+)
+
+add_custom_command(
+        TARGET render POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_directory "${CMAKE_SOURCE_DIR}/src/render/backend/demo/" "${CMAKE_BINARY_DIR}/render/"
+)

examples/battle_model/README.md

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+# Battle Game
+
+A cooperative and competitive senario implemented based on MAgent platform 
+
+## Code structure
+
+- `./algo`: contains algorithms (MFAC, AC, MFQ, IL)
+
+- `./python` and `./src`: contains engine files
+
+- `./senario_battle.py`: contains code for the logic of play Battle Game
+
+- `./build.sh`: you can run it to compile the MAgent platform

examples/battle_model/algo/__init__.py

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+from . import q_learning
+
+IL = q_learning.DQN
+MSGDQN = q_learning.MsgDQN
+GIL=q_learning.GDQN
+
+def spawn_ai(algo_name, sess, env, handle, human_name, max_steps,len_nei=6):
+    if algo_name == 'il':
+        model = IL(sess, human_name, handle, env, max_steps, len_nei,memory_size=80)
+    elif algo_name == 'gil':
+        model = GIL(sess, human_name, handle, env, max_steps,len_nei, memory_size=8,isHie=False)
+    elif algo_name == 'msgdqn':     
+        model=MSGDQN(sess,human_name,handle,env,max_steps,len_nei,msg_bits=3,memory_size=80000)
+
+    return model