Answers.Body: <ol>
  <li>State Encoding:</li>
  </ol>
  <ul>
  <li>Instead of using the raw grid, consider encoding the difference between current and target states</li>
  <li>Use techniques like run-length encoding to compress similar regions</li>
  <li>Consider using autoencoders to learn a lower-dimensional representation of the state</li>
  <li>You might want to include some local features around possible placement positions</li>
  </ul>
  <ol start="2">
  <li>Action Space Reduction:</li>
  </ol>
  <ul>
  <li>Instead of allowing placements anywhere, you could identify &quot;interesting&quot; positions first (e.g., areas where current and target states differ)</li>
  <li>Create a hierarchical approach: first, select the grid piece, then select the region to focus on, then select the  exact position and rotation</li>
  <li>You might want to use a curriculum learning approach, starting with smaller grids and simpler patterns</li>
  </ul>
  <ol start="3">
  <li>Algorithm Suggestions:</li>
  </ol>
  <ul>
  <li>Given the large action space, Q-learning based approaches might struggle</li>
  <li>Consider trying:
  <ul>
  <li>Monte Carlo Tree Search (MCTS) combined with deep learning (similar to AlphaGo's approach)</li>
  <li>Hierarchical Reinforcement Learning (HRL)</li>
  <li>Model-based RL approaches that can plan ahead</li>
  </ul>
  </li>
  </ul>
  <p>Here's a specific approach you might want to try:</p>
  <pre><code># Example of a hierarchical state/action encoding
  def encode_state(current_grid, target_grid, available_pieces):
      # Encode difference between current and target
      difference = target_grid - current_grid
      
      # Find regions of interest (where differences exist)
      regions = identify_regions(difference)
      
      # Create features for each region
      region_features = []
      for region in regions:
          # Encode local pattern
          local_pattern = extract_local_pattern(region)
          # Match against available pieces
          piece_matches = match_pieces(local_pattern, available_pieces)
          region_features.append((local_pattern, piece_matches))
      
      return region_features
  
  def hierarchical_action(state):
      # Level 1: Choose region to focus on
      region = select_region(state.regions)
      
      # Level 2: Choose piece to place
      piece = select_piece(region.piece_matches)
      
      # Level 3: Choose exact position and rotation
      position, rotation = fine_tune_placement(region, piece)
      
      return position, piece, rotation
  </code></pre>
  <p>For your implementation:</p>
  <ol>
  <li><p>Start with a smaller grid size (e.g., 32x32) for initial testing</p>
  </li>
  <li><p>Implement curriculum learning:</p>
  </li>
  </ol>
  <ul>
  <li>Begin with simple patterns</li>
  <li>Gradually increase complexity</li>
  <li>Slowly increase grid size</li>
  </ul>
  <ol start="3">
  <li>Consider using a hybrid approach:</li>
  </ol>
  <ul>
  <li>Use heuristics to reduce the action space</li>
  <li>Apply RL on the reduced space</li>
  <li>Gradually reduce the heuristic constraints as the agent improves</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-12-26T04:21:09.037Z
Answers.LastActivityDate: 2024-12-26T04:21:09.037Z
Answers.UserDisplayName: codex
Body: <p>Game Summary:</p>
  <p>Objective: The game provides me with a 2D grid (with a maximum size of up to 256 x 256). My task is to transform this 2D grid into another target grid.</p>
  <p>About the actions: I am given 25 (or possibly more) additional 2D grids of varying sizes. I can choose any grid and place it at a position (x, y) that satisfies the conditions. After placing it, I can choose one of four directions for that action. As you can see, the action space of the game is incredibly large.</p>
  <p>If you're interested, you can read more about the game details <a href="https://drive.google.com/file/d/10zKvcdfVI7tGnrD2grFircZI0O8dNQ8-/view" rel="nofollow noreferrer">here</a>.</p>
  <p>What I have done so far: I have built my custom environment using Gymnasium and applied the PPO algorithm from the Stable Baselines3 library. However, as you might expect, the agent hasn't learned anything at all.</p>
  <p>Issues I need advice and help with: As you can see, the action space and state space of the game are enormous. So, which algorithm would be the most suitable for this game?</p>
  <p>How can I encode the states into something more compact? Moreover, the continuously changing states seem to affect the learning performance.</p>
  <p>Please note, I am not an expert; I am just a student starting to learn about reinforcement learning.</p>
  
Tags: <reinforcement-learning><deep-rl><game-ai>
Title: Reinforcement learning model with games that have very large state and action spaces
UserDisplayName: Hiếu Bùi Trung
ThreadText: # Reinforcement learning model with games that have very large state and action spaces
  
  (Asked by: username_0 on 2024-11-19)
  
  Game Summary:
  
  Objective: The game provides me with a 2D grid (with a maximum size of up to 256 x 256). My task is to transform this 2D grid into another target grid.
  
  About the actions: I am given 25 (or possibly more) additional 2D grids of varying sizes. I can choose any grid and place it at a position (x, y) that satisfies the conditions. After placing it, I can choose one of four directions for that action. As you can see, the action space of the game is incredibly large.
  
  If you're interested, you can read more about the game details [here](https://drive.google.com/file/d/10zKvcdfVI7tGnrD2grFircZI0O8dNQ8-/view).
  
  What I have done so far: I have built my custom environment using Gymnasium and applied the PPO algorithm from the Stable Baselines3 library. However, as you might expect, the agent hasn't learned anything at all.
  
  Issues I need advice and help with: As you can see, the action space and state space of the game are enormous. So, which algorithm would be the most suitable for this game?
  
  How can I encode the states into something more compact? Moreover, the continuously changing states seem to affect the learning performance.
  
  Please note, I am not an expert; I am just a student starting to learn about reinforcement learning.
  
  **Tags:** reinforcement-learning, deep-rl, game-ai
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-12-26. Score: 0
  
  1. State Encoding:
  
  * Instead of using the raw grid, consider encoding the difference between current and target states
  * Use techniques like run-length encoding to compress similar regions
  * Consider using autoencoders to learn a lower-dimensional representation of the state
  * You might want to include some local features around possible placement positions
  
  2. Action Space Reduction:
  
  * Instead of allowing placements anywhere, you could identify "interesting" positions first (e.g., areas where current and target states differ)
  * Create a hierarchical approach: first, select the grid piece, then select the region to focus on, then select the exact position and rotation
  * You might want to use a curriculum learning approach, starting with smaller grids and simpler patterns
  
  3. Algorithm Suggestions:
  
  * Given the large action space, Q-learning based approaches might struggle
  * Consider trying:
    + Monte Carlo Tree Search (MCTS) combined with deep learning (similar to AlphaGo's approach)
    + Hierarchical Reinforcement Learning (HRL)
    + Model-based RL approaches that can plan ahead
  
  Here's a specific approach you might want to try:
  
  ```
  # Example of a hierarchical state/action encoding
  def encode_state(current_grid, target_grid, available_pieces):
      # Encode difference between current and target
      difference = target_grid - current_grid
      
      # Find regions of interest (where differences exist)
      regions = identify_regions(difference)
      
      # Create features for each region
      region_features = []
      for region in regions:
          # Encode local pattern
          local_pattern = extract_local_pattern(region)
          # Match against available pieces
          piece_matches = match_pieces(local_pattern, available_pieces)
          region_features.append((local_pattern, piece_matches))
      
      return region_features
  
  def hierarchical_action(state):
      # Level 1: Choose region to focus on
      region = select_region(state.regions)
      
      # Level 2: Choose piece to place
      piece = select_piece(region.piece_matches)
      
      # Level 3: Choose exact position and rotation
      position, rotation = fine_tune_placement(region, piece)
      
      return position, piece, rotation
  
  ```
  
  For your implementation:
  
  1. Start with a smaller grid size (e.g., 32x32) for initial testing
  2. Implement curriculum learning:
  
  * Begin with simple patterns
  * Gradually increase complexity
  * Slowly increase grid size
  
  3. Consider using a hybrid approach:
  
  * Use heuristics to reduce the action space
  * Apply RL on the reduced space
  * Gradually reduce the heuristic constraints as the agent improves
  
  
  ---
  
  