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Adam Krawitz
Assistant Teaching Professor, Department of Psychology, University of Victoria, British Columbia, Canada. My academic interests include cognitive neuroscience, computational modeling, and explorable explanations.
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UVic HINF 485 Demonstrations
Edited
May 8, 2023
2
UVic HINF 485 Demonstrations
HINF 485 Demo: Feedforward NetworksHINF 485 Demo: Self-organizing Maps
HINF 485 Demo: Reinforcement Learning
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simLine = actorCriticSim(this, conLine, {states: lineStates, rewards: lineRewards, actions: lineActions, parameters: {alpha: 0.5, beta: 0.5, gamma: 0.8, epsilon: 0.2}})
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simGrid = actorCriticSim(this, conGrid, {states: gridStates, rewards: gridRewards, actions: gridActions, parameters: {alpha: 0.5, beta: 0.5, gamma: 0.8, epsilon: 0.2}})
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lineStates = ({
rows: 1,
columns: 10,
beginStates: [[0, 0]],
endStates: [[0, 9]],
wallStates: [],
})
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lineRewards = ({
moveReward: 0,
edgeReward: 0,
wallReward: 0,
stateRewards: [{reward: 100, states: lineStates.endStates}],
})
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lineActions = ({
actions: [
actorCriticEnum.ACTIONS.LEFT,
actorCriticEnum.ACTIONS.RIGHT,
],
});
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gridStates = ({
rows: 5,
columns: 6,
beginStates: [[0, 0]],
endStates: [[0, 5]],
wallStates: [[0, 2], [1, 2], [2, 2]],
})
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gridRewards = ({
moveReward: -1,
edgeReward: -5,
wallReward: -5,
stateRewards: [{reward: 100, states: gridStates.endStates}, {reward: -100, states: [[3, 5]]}],
})
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gridActions = ({
actions: [
actorCriticEnum.ACTIONS.LEFT,
actorCriticEnum.ACTIONS.UP,
actorCriticEnum.ACTIONS.RIGHT,
actorCriticEnum.ACTIONS.DOWN,
],
});
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