Reinforcement learning approach to the prisoner's dilemma, based on Q learning
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Updated
Dec 1, 2017 - Python
Reinforcement learning approach to the prisoner's dilemma, based on Q learning
Recreation of the prisoner's dilemma model from Axelrod's "Evolution of Cooperation" in Python
A prisoner's dilemma agent based model simulation for investigating effects of differing strategies on emergent behaviours and spatial patterns with configurable environments.
This Python program simulates the Prisoner's Dilemma, allowing players to choose from various strategies (Cooperator, Defector, Revenger, Tit-for-Tat, Random, and Detective). Players can specify the number of rounds, with an optional delay to observe each round’s interactions.
Simulator for the classical prisoner dilemma game.
Implements several strategies and the overall tournament functions
Prisoner's-Dilemma-Tournament-Mode
An implementation of iterative prisoner dilemma with reinforcement learning via q_table (NOT DQN) in Python with around 30 strategies
Iterative Prisoner Dilemma - Tournament of 20+ classic strategies and an ML player built with DQN
The prisoner's dilemma in python
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