Approach.md

Certainly! Here's an analysis and approach for coding the rock, paper, scissors project:

1. 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.

2. 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

3. 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

4. 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

5. 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

6. 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.