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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,7 @@ | ||
| from .dist_open_es import DistributedOpenES | ||
| from .open_es import OpenES | ||
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| DistributedStrategies = {"DistributedOpenES": DistributedOpenES} | ||
| DistributedStrategies = {"OpenES": OpenES} | ||
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| __all__ = ["DistributedOpenES", "DistributedStrategies"] | ||
| __all__ = ["OpenES", "DistributedStrategies"] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| from typing import Tuple, Optional, Union | ||
| import jax | ||
| import jax.numpy as jnp | ||
| import chex | ||
| from flax import struct | ||
| from ..strategy import Strategy | ||
| from ..utils import get_best_fitness_member | ||
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| @struct.dataclass | ||
| class EvoState: | ||
| mean: chex.Array | ||
| sigma: chex.Array | ||
| best_member: chex.Array | ||
| best_fitness: float = jnp.finfo(jnp.float32).max | ||
| gen_counter: int = 0 | ||
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| @struct.dataclass | ||
| class EvoParams: | ||
| sigma_init: float = 1.0 | ||
| init_min: float = 0.0 | ||
| init_max: float = 0.0 | ||
| clip_min: float = -jnp.finfo(jnp.float32).max | ||
| clip_max: float = jnp.finfo(jnp.float32).max | ||
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| class HillClimber(Strategy): | ||
| def __init__( | ||
| self, | ||
| popsize: int, | ||
| num_dims: Optional[int] = None, | ||
| pholder_params: Optional[Union[chex.ArrayTree, chex.Array]] = None, | ||
| mean_decay: float = 0.0, | ||
| n_devices: Optional[int] = None, | ||
| **fitness_kwargs: Union[bool, int, float] | ||
| ): | ||
| """Simple Gaussian Hill Climbing""" | ||
| super().__init__( | ||
| popsize, num_dims, pholder_params, mean_decay, n_devices, **fitness_kwargs | ||
| ) | ||
| self.strategy_name = "HillClimber" | ||
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| @property | ||
| def params_strategy(self) -> EvoParams: | ||
| """Return default parameters of evolution strategy.""" | ||
| return EvoParams() | ||
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| def initialize_strategy(self, rng: chex.PRNGKey, params: EvoParams) -> EvoState: | ||
| """`initialize` the evolution strategy.""" | ||
| initialization = jax.random.uniform( | ||
| rng, | ||
| (self.num_dims,), | ||
| minval=params.init_min, | ||
| maxval=params.init_max, | ||
| ) | ||
| state = EvoState( | ||
| mean=initialization, | ||
| sigma=params.sigma_init * jnp.ones((self.num_dims,)), | ||
| best_member=initialization, | ||
| ) | ||
| return state | ||
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| def ask_strategy( | ||
| self, rng: chex.PRNGKey, state: EvoState, params: EvoParams | ||
| ) -> Tuple[chex.Array, EvoState]: | ||
| """`ask` for new proposed candidates to evaluate next.""" | ||
| # Sampling of N(0, 1) noise | ||
| z = jax.random.normal( | ||
| rng, | ||
| (self.popsize, self.num_dims), | ||
| ) | ||
| x = state.best_member + state.sigma.reshape(1, self.num_dims) * z | ||
| return x, state | ||
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| def tell_strategy( | ||
| self, | ||
| x: chex.Array, | ||
| fitness: chex.Array, | ||
| state: EvoState, | ||
| params: EvoParams, | ||
| ) -> EvoState: | ||
| """`tell` update to ES state.""" | ||
| best_member, best_fitness = get_best_fitness_member(x, fitness, state, False) | ||
| return state.replace(mean=best_member) |
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