#2777: Implementation of RNG seed
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@@ -22,8 +22,6 @@ class ProbabilisticAgent(AbstractScriptedAgent):
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"""Strict validation."""
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action_probabilities: Dict[int, float]
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"""Probability to perform each action in the action map. The sum of probabilities should sum to 1."""
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random_seed: Optional[int] = None
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"""Random seed. If set, each episode the agent will choose the same random sequence of actions."""
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# TODO: give the option to still set a random seed, but have it vary each episode in a predictable way
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# for example if the user sets seed 123, have it be 123 + episode_num, so that each ep it's the next seed.
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@@ -59,17 +57,19 @@ class ProbabilisticAgent(AbstractScriptedAgent):
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num_actions = len(action_space.action_map)
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settings = {"action_probabilities": {i: 1 / num_actions for i in range(num_actions)}}
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# If seed not specified, set it to None so that numpy chooses a random one.
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settings.setdefault("random_seed")
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# The random number seed for np.random is dependent on whether a random number seed is set
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# in the config file. If there is one it is processed by set_random_seed() in environment.py
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# and as a consequence the the sequence of rng_seed's used here will be repeatable.
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self.settings = ProbabilisticAgent.Settings(**settings)
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self.rng = np.random.default_rng(self.settings.random_seed)
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rng_seed = np.random.randint(0, 65535)
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self.rng = np.random.default_rng(rng_seed)
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print(f"Probabilistic Agent - rng_seed: {rng_seed}")
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# convert probabilities from
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self.probabilities = np.asarray(list(self.settings.action_probabilities.values()))
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super().__init__(agent_name, action_space, observation_space, reward_function)
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self.logger.info(f"ProbabilisticAgent RNG seed: {rng_seed}")
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def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
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"""
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@@ -70,6 +70,8 @@ class PrimaiteGameOptions(BaseModel):
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model_config = ConfigDict(extra="forbid")
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seed: int = None
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"""Random number seed for RNGs."""
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max_episode_length: int = 256
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"""Maximum number of episodes for the PrimAITE game."""
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ports: List[str]
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