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https://github.com/vale981/ray
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92 lines
2.5 KiB
Python
92 lines
2.5 KiB
Python
import gym
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import numpy as np
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import random
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from ray.rllib.policy.policy import Policy
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from ray.rllib.policy.view_requirement import ViewRequirement
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ROCK = 0
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PAPER = 1
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SCISSORS = 2
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class AlwaysSameHeuristic(Policy):
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"""Pick a random move and stick with it for the entire episode."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.exploration = self._create_exploration()
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self.view_requirements.update(
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{
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"state_in_0": ViewRequirement(
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"state_out_0",
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shift=-1,
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space=gym.spaces.Box(0, 100, shape=(), dtype=np.int32),
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)
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}
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)
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def get_initial_state(self):
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return [random.choice([ROCK, PAPER, SCISSORS])]
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def compute_actions(
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self,
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obs_batch,
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state_batches=None,
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prev_action_batch=None,
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prev_reward_batch=None,
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info_batch=None,
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episodes=None,
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**kwargs
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):
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return state_batches[0], state_batches, {}
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class BeatLastHeuristic(Policy):
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"""Play the move that would beat the last move of the opponent."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.exploration = self._create_exploration()
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def compute_actions(
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self,
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obs_batch,
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state_batches=None,
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prev_action_batch=None,
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prev_reward_batch=None,
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info_batch=None,
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episodes=None,
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**kwargs
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):
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def successor(x):
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# Make this also work w/o one-hot preprocessing.
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if isinstance(self.observation_space, gym.spaces.Discrete):
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if x == ROCK:
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return PAPER
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elif x == PAPER:
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return SCISSORS
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elif x == SCISSORS:
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return ROCK
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else:
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return random.choice([ROCK, PAPER, SCISSORS])
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# One-hot (auto-preprocessed) inputs.
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else:
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if x[ROCK] == 1:
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return PAPER
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elif x[PAPER] == 1:
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return SCISSORS
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elif x[SCISSORS] == 1:
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return ROCK
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elif x[-1] == 1:
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return random.choice([ROCK, PAPER, SCISSORS])
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return [successor(x) for x in obs_batch], [], {}
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def learn_on_batch(self, samples):
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pass
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def get_weights(self):
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pass
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def set_weights(self, weights):
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pass
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