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Project 4 - Mario

This program utilizes Monte Carlo Reinforcement Learning to allow the agent (Mario) to learn how to reach the goal in a platformer level.

Quick Start

Navigate to 15-rl-competition-2009. Ensure everything is installed by running ./install.bash.

Once everything is installed, let's run the program. Open 2 terminal windows.

In terminal 1 (agent terminal):

  1. Navigate to 15-rl-competition-2009/agents/marioAgentJava.
  2. Run make.
  3. Run ./run.bash.

In terminal 2 (trainer terminal):

  1. Navigate to 15-rl-competition-2009/trainers/consoleTrainerJava.
  2. Run make.
  3. Run ./run.bash.

The agent will begin training with the following configurations:

  • episodes: 0
  • level seed: 0
  • level type: 0
  • level difficulty: 0

Note: if the action idle is being preferred very often, I would recommend retrying. Otherwise it may take longer for the agent to converge towards moving forward actions.

Once the program finishes running, two files will be generated:

  • ref_export.json - This file contains the following exported variables:
    • total_reward
    • policy_table
    • reward_table
    • policy_itr_table
  • debug_log.json - Contains variables and states used for debugging.

Transfer Learning

When running terminal 1 (agent terminal) ./run.bash again, the program will automatically read ref_export.json and carry on the knowledge from the previous run. This is not dependent on the level seed, type, and difficulty; therefore, knowledge from a previous level can be tested on a new level. The file must be named ref_export.json in order for transfer learning to work.

To start from scratch, in terminal 1 (agent terminal) either

  • rename ref_export.json or
  • run make clean, then make and ./run.bash

States

The following states are implemented and utilized in the agent:

  • MONSTER_NEAR - checks if an enemy is within 5 tiles in front and 1 tile behind Mario.
  • MONSTER_FAR - checks if an enemy is within 3 - 11 tiles in front.
    • Note: the ranges of MONSTER_NEAR and MONSTER_FAR overlap. This creates a scenario where the monster is moderately close to Mario without the need for another separate state.
  • MONSTER_ABOVE - checks if an enemy has higher ground than Mario.
  • MONSTER_BELOW - checks if an enemy has lower ground than Mario.
  • PIT_FAR - checks if a pit is within 3 - 5 tiles in front from Mario.
  • PIT_NEAR - checks if a pit is within 0 - 2 tiles in front from Mario.
  • QUESTION_BLOCK - checks for an item block in an area 7 tiles in front and 5 tiles above Mario.
  • BONUS_ITEM_FAR - checks if a Mushroom or Fire Flower is greater than 3 tiles in front or behind Mario.
  • BONUS_ITEM_NEAR - checks if a Mushroom or Fire Flower is less than or equal to 3 tiles in front or behind Mario
  • COINS - checks for coins in an area 7 tiles in front and 5 tiles above Mario.
  • BREAKABLE_BLOCK - checks for a breakable block in an area 7 tiles in front and 5 tiles above Mario.
  • HIGHER_GROUND_NEAR - checks if there is an impassible tile 3 - 6 tiles in front of Mario.
  • HIGHER_GROUND_FAR - checks if there is an impassible tile 0 - 2 tiles in front of Mario.

Policy

Mario will have a 25% chance to explore. He will randomly choose actions in these three categories:

  • Moving- backwards, forwards, or idle
  • Jumping - to jump or not to jump...
  • Speed - walking or running.
    • If idle is selected from moving, Mario will still be idle.

Otherwise, based on the states (his surroundings), Mario will choose and execute the combination of actions in the policy table that contains the highest reward.

Modifying Configurations

In terminal 2 (trainer terminal), open 15-rl-competition-2009/trainers/consoleTrainerJava/src/consoleTrainer.java. The following may be changed to alter the level:

  • episodeCount: 0
  • In consoleTrainerHelper.loadMario():
    • level seed
    • level type
    • level difficulty
    • level instance

Agent Code

In terminal 1 (agent terminal), the agent code can be found at 15-rl-competition-2009/agents/marioAgentJava/src/edu/rutgers/rl3/comp/ExMarioAgent.java

References

The Mario Project: https://eecs.wsu.edu/~taylorm/11EAAI/