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from typing import Dict, Optional, Union | |
from fragile.core import DiscreteEnv | |
import gym | |
from gym import spaces | |
import numpy as np | |
from mathy import MathTypeKeysMax, MathyEnvState | |
from mathy.envs.gym import MathyGymEnv |
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This game was played applying the causal entropic forces principles (http://www.alexwg.org/publications/PhysRevLett_110-168702.pdf). | |
We approximated the solutions of the proposed equation using the method described here (https://arxiv.org/abs/1705.08691). | |
No neural networks were involved in this solutions. Everything was sampled using our "Fractal GAS" method, using only the cpu of | |
a laptop. Since we did not implement any memory in the system, each game is independent of the other games played. | |
We would like some help, so we can use the games sampled using our method to train an A3C efficiently. As the "Fractal GAS" | |
allow us to sample human-like games from a uniform prior, we would be able to transform any RL problem into a supervised learning task. | |
If you want us to share the code please show some interest and contact us in our blog http://entropicai.blogspot.com.es/ |