#2777: Implementation of RNG seed

src/primaite/game/agent/scripted_agents/probabilistic_agent.py

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

src/primaite/game/game.py

@@ -70,6 +70,8 @@ class PrimaiteGameOptions(BaseModel):
 
     model_config = ConfigDict(extra="forbid")
 
+    seed: int = None
+    """Random number seed for RNGs."""
     max_episode_length: int = 256
     """Maximum number of episodes for the PrimAITE game."""
     ports: List[str]

src/primaite/session/environment.py

@@ -1,5 +1,7 @@
 # © Crown-owned copyright 2024, Defence Science and Technology Laboratory UK
 import json
+import random
+import sys
 from os import PathLike
 from typing import Any, Dict, Optional, SupportsFloat, Tuple, Union
 
@@ -17,6 +19,33 @@ from primaite.simulator.system.core.packet_capture import PacketCapture
 
 _LOGGER = getLogger(__name__)
 
+# Check torch is installed
+try:
+    import torch as th
+except ModuleNotFoundError:
+    _LOGGER.debug("Torch not available for importing")
+
+
+def set_random_seed(seed: int) -> Union[None, int]:
+    """
+    Set random number generators.
+
+    :param seed: int
+    """
+    if seed is None or seed == -1:
+        return None
+    elif seed < -1:
+        raise ValueError("Invalid random number seed")
+    # Seed python RNG
+    random.seed(seed)
+    # Seed numpy RNG
+    np.random.seed(seed)
+    # Seed the RNG for all devices (both CPU and CUDA)
+    # if torch not installed don't set random seed.
+    if sys.modules["torch"]:
+        th.manual_seed(seed)
+    return seed
+
 
 class PrimaiteGymEnv(gymnasium.Env):
     """
@@ -31,6 +60,9 @@ class PrimaiteGymEnv(gymnasium.Env):
         super().__init__()
         self.episode_scheduler: EpisodeScheduler = build_scheduler(env_config)
         """Object that returns a config corresponding to the current episode."""
+        self.seed = self.episode_scheduler(0).get("game").get("seed")
+        """Get RNG seed from config file. NB: Must be before game instantiation."""
+        self.seed = set_random_seed(self.seed)
         self.io = PrimaiteIO.from_config(self.episode_scheduler(0).get("io_settings", {}))
         """Handles IO for the environment. This produces sys logs, agent logs, etc."""
         self.game: PrimaiteGame = PrimaiteGame.from_config(self.episode_scheduler(0))
@@ -42,6 +74,8 @@ class PrimaiteGymEnv(gymnasium.Env):
         self.total_reward_per_episode: Dict[int, float] = {}
         """Average rewards of agents per episode."""
 
+        _LOGGER.info(f"PrimaiteGymEnv RNG seed = {self.seed}")
+
     def action_masks(self) -> np.ndarray:
         """
         Return the action mask for the agent.
@@ -108,6 +142,8 @@ class PrimaiteGymEnv(gymnasium.Env):
             f"Resetting environment, episode {self.episode_counter}, "
             f"avg. reward: {self.agent.reward_function.total_reward}"
         )
+        if seed is not None:
+            set_random_seed(seed)
         self.total_reward_per_episode[self.episode_counter] = self.agent.reward_function.total_reward
 
         if self.io.settings.save_agent_actions:

src/primaite/session/ray_envs.py

@@ -63,6 +63,7 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
 
     def reset(self, *, seed: int = None, options: dict = None) -> Tuple[ObsType, Dict]:
         """Reset the environment."""
+        super().reset()  # Ensure PRNG seed is set everywhere
         rewards = {name: agent.reward_function.total_reward for name, agent in self.agents.items()}
         _LOGGER.info(f"Resetting environment, episode {self.episode_counter}, " f"avg. reward: {rewards}")
 
@@ -176,6 +177,7 @@ class PrimaiteRayEnv(gymnasium.Env):
 
     def reset(self, *, seed: int = None, options: dict = None) -> Tuple[ObsType, Dict]:
         """Reset the environment."""
+        super().reset()  # Ensure PRNG seed is set everywhere
         if self.env.agent.action_masking:
             obs, *_ = self.env.reset(seed=seed)
             new_obs = {"action_mask": self.env.action_masks(), "observations": obs}