HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.
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Monte Carlo Tree Search :: AlphaGeese, AlphaZero .. etc #356
Page 16..
AlphaGeese [2] follows an implementation of MCTS [21] [22]. The implementation of MCTS tracks the following variables.
• P(s, a, i) The prior probability agent i of taking an action a from a state
s according to the neural network. This is the softmaxed value of the
action-values inferred by the neural network.
• N(s, a, i) The number of times we explore the action a taken by agent i
from a state s when we are searching the tree.
• Q(s, a, i) The expected reward for taking the action a by agent i from
a state s. This is initialised with the state-value inferred by the neural
network. Q(s, a, i) is the average of the state-values of the explored nodes
in its subtree.
The action with the highest upper confidence bound U(s, a) is explored.
It would be nice to have an implementation of the MCTS in this codebase. In case it is not clear, I am referring to the https://tonghuikang.github.io/ai-project/report.pdf.
Page 16.. AlphaGeese [2] follows an implementation of MCTS [21] [22]. The implementation of MCTS tracks the following variables. • P(s, a, i) The prior probability agent i of taking an action a from a state s according to the neural network. This is the softmaxed value of the action-values inferred by the neural network. • N(s, a, i) The number of times we explore the action a taken by agent i from a state s when we are searching the tree. • Q(s, a, i) The expected reward for taking the action a by agent i from a state s. This is initialised with the state-value inferred by the neural network. Q(s, a, i) is the average of the state-values of the explored nodes in its subtree. The action with the highest upper confidence bound U(s, a) is explored.