This post bridges two domains, game theory and reinforcement learning, which previously I previously thought of as mostly separate; and it caused a pretty big shift in my model of how intelligence-in-general works, since this is much simpler than my previous simplest model of how reinforcement learning would do game theory.
This post bridges two domains, game theory and reinforcement learning, which previously I previously thought of as mostly separate; and it caused a pretty big shift in my model of how intelligence-in-general works, since this is much simpler than my previous simplest model of how reinforcement learning would do game theory.
Reinforcement learning is not required for the analysis above. Only evolutionary game theory is needed.
In evolutionary game theory, the population’s mix of strategies changes via replicator dynamics.
In RL, each individual agent modifies its policy as it interacts with its environment using a learning algorithm.