neural network to play minecraft in python

To train a neural network to play Minecraft using reinforcement learning, we can use the Malmo platform, which provides a Minecraft mod with a Python API that allows us to control the game and receive information about the game state.

Here's a general overview of how to proceed:

Install Malmo from https://github.com/Microsoft/malmo/
Create a Minecraft environment, initialize the agent and the neural network
Define the action space and reward function
Run the Minecraft game in training mode, letting the agent interact with the environment and updating the neural network parameters based on its experience.

Below is some sample code to give an idea of what this process might look like:


main.py
import malmo.MalmoPython as Malmo
import json

# initialize Minecraft client
client_pool = Malmo.ClientPool()
client = Malmo.ClientInfo('127.0.0.1', 10000)
client_pool.add(client)

# initialize neural net
model = NN()

# define action space
actions = ['turn -1', 'turn 1', 'move 1', 'move -1', 'jump', 'attack']

# define reward function
def get_reward(state):
    if state['fell']:
        return -100.0
    elif state['found_goal']:
        return 100.0
    else:
        return -1.0

# start Minecraft game in training mode
agent_host = Malmo.AgentHost()
agent_host.setObservationsPolicy(Malmo.ObservationsPolicy.LATEST_OBSERVATION)
my_mission = Malmo.MissionSpec(mission_xml, True)
my_mission.forceWorldReset()
my_mission.timeLimitInSeconds(300)
my_mission_record = Malmo.MissionRecordSpec()

# run the game loop
for i in range(num_epochs):
    agent_host.startMission(my_mission, client_pool, my_mission_record, i, "%s-%d" % (mission_name, i))
    world_state = agent_host.getWorldState()
    t = 0
    reward = 0.0
    while world_state.is_mission_running:
        if world_state.number_of_observations_since_last_state > 0:
            state = json.loads(world_state.observations[-1].text)
            q_values = model.predict(state)
            action = actions[np.argmax(q_values)]
            agent_host.sendCommand(action)
            world_state = agent_host.getWorldState()
            next_state = json.loads(world_state.observations[-1].text)
            reward += get_reward(next_state)
            model.update(state, action, next_state, reward)
            t += 1
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Note that this is just a simplified example, and actual implementation may require additional tweaks and modifications.

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related categories

reinforcement learning