DQN Bot Integration with Playroom

Overview

Enter the DQNBotJoystick, an extension of the already robust DQNBot offered by Playroom's SDK. This specialized bot class harmoniously marries the capabilities of the DQN with the intuitive control of a joystick. By allowing developers to input joystick configurations, the gameplay experience becomes more fluid, while still harnessing the intelligence of reinforcement learning.

How DQN Bot with Joystick Integration Works

1. Import DQN Bot with Joystick

Start by importing the 'DQNBotJoystick' from the 'playroomkit'

import { insertCoin, onPlayerJoin, DQNBotJoystick } from 'playroomkit';

2. Initialization Using insertCoin

Initiate the joystick-augmented DQN bot by using the insertCoin method, making sure to include your joystick configuration:

insertCoin({
  ... other parameters,
  
  enableBots: true, // Signals the SDK to activate the DQN bot
  
  botOptions: {
    botClass: DQNBotJoystick,  // Specifies the DQN bot class
    
    numberOfStates: x,  // Replace 'x' with the number of states you want to pass to the agent.
 
    // Your joystick configuration here. Actions will be derived from this joystick config.
    joystickConfig: { // Sample configuration
      type: "dpad",
      buttons: [
        {id: "jump", label: "Jump"}
      ]
    }
 
    // OPTIONAL: Weights, training mode, and other configurations can also be passed as DQN Bot
  },
}).then(() => {
  // Callback or subsequent logic after the bot initialization completes
});

3. Fluid Game Loop Integration with Joystick Control

Interweave the joystick-driven bot decisions within your game loop to achieve a seamless gameplay experience:

if (player.isBot()) {
    const currentState = [/* array of numbers or booleans defining the game state */];
    
    // With the joystick configuration in place, just set the game state
    player.setDQNBotState(currentState);
    
    // The DQNBotJoystick, equipped with your joystick configuration, will automatically set the corresponding action
    // No need to explicitly extract or set the action
 
    // Reward the bot based on the outcome of its action, refining its decision-making process
    const rewardValue = computeRewardForJoystickAction();
    player.setDQNBotReward(rewardValue);
}

To train the bot, continually provide rewards based on its actions to refine its decision-making capabilities. If you wish to use the bot without additional training, simply utilize the DQN bot's decisions without supplying reward feedback.

Conclusion

The DQNBotJoystick offers game developers an amplified experience, combining the strategic depth of DQN with the tangible and immediate feedback of joystick controls. Whether you're crafting intricate game scenarios or merely exploring reinforcement learning's potential, the joystick-augmented DQN bot promises a dynamic gaming adventure.