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View Code? Open in Web Editor NEWAdaptive Mechanism Design (AMD) in Sequential Social Dilemma (SSD). Foundations of Reinforcement Learning 2023 Spring, team project.
Adaptive Mechanism Design (AMD) in Sequential Social Dilemma (SSD). Foundations of Reinforcement Learning 2023 Spring, team project.
The original SSD paper have two social dilema
In vermashresth's SSD, they have three other social dilemmas
In social-dilemma/multiagent, there are three ssd
In eugenevinitsky's ssd, they also include watershed and cleanup
From the paper, we know each agent's pseudo policy gradient assumed by the central planner
where
The reward given by planner is a function of current states,
The planner update its parameter by a similar policy gradient
Here I call
pettingzoo.utils.env.ParallelEnv
, convert it to ray.rllib.env.multi_agent_env.MultiAgentEnv
. [source]gym.spaces.Space
for unflatten rewards), and identify central planner to the algorithm's worker. This is achieved by a rllib callback. [source]The whole training and configuration script is shown here.
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