Certainly! Here's an analysis and approach for coding the rock, paper, scissors project:
- Understanding the problem:
- We need to create a player function that can win against four different opponents (quincy, abbey, kris, and mrugesh) at least 60% of the time.
- The player function should take the opponent's previous play as an input and return the next move.
- We need to implement different strategies to counter various playing styles.
- Analyzing the opponents:
- Quincy: Follows a fixed pattern (R, R, P, P, S)
- Mrugesh: Plays the move that would have beaten the most frequent move in the last 10 rounds
- Kris: Plays the move that would have beaten the opponent's last move
- Abbey: Uses a more complex strategy based on play order and frequency
- Approach to implement the player function:
- Use the opponent_history to keep track of all previous plays
- Implement multiple strategies and switch between them based on the opponent's behavior
- Use pattern recognition to identify and exploit predictable sequences
- Implement a counter-strategy for each known opponent
- Strategies to consider:
- Pattern matching: Look for repeating sequences in the opponent's moves
- Frequency analysis: Track the frequency of each move and counter the most common one
- Adaptive play: Adjust strategy based on the success rate of different moves
- Meta-strategy: Detect which opponent we're playing against and use a specific counter-strategy
- Implementation steps:
- Initialize variables to track opponent history, move frequencies, and strategy performance
- Implement functions for each strategy (e.g., pattern matching, frequency analysis)
- Create a main logic that selects the best strategy based on performance
- Add logic to detect specific opponents and apply tailored counter-strategies
- Continuously update and refine the strategy selection based on win rates
- Testing and refinement:
- Use the play function in main.py to test against each opponent
- Analyze the results and identify weaknesses in the current strategy
- Refine and adjust the strategies to improve win rates
- Ensure the player function passes all unit tests in test_module.py
By implementing this approach, we can create a robust player function that adapts to different opponents and achieves a high win rate across all matchups.