#1386: added the ability to set deterministic and seeding RNG when training and evaluating + the fix provided in #1535

src/primaite/main.py

@@ -14,8 +14,8 @@ from pathlib import Path
 from typing import Final, Union
 from uuid import uuid4
 
+import numpy as np
 from stable_baselines3 import A2C, PPO
-from stable_baselines3.common.evaluation import evaluate_policy
 from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
 from stable_baselines3.ppo import MlpPolicy as PPOMlp
 
@@ -54,9 +54,6 @@ def run_generic(env: Primaite, config_values: TrainingConfig):
 
             # Introduce a delay between steps
             time.sleep(config_values.time_delay / 1000)
-
-        # Reset the environment at the end of the episode
-
     env.close()
 
 
@@ -90,7 +87,7 @@ def run_stable_baselines3_ppo(
             _LOGGER.error("Could not load agent")
             _LOGGER.error("Exception occured", exc_info=True)
     else:
-        agent = PPO(PPOMlp, env, verbose=0, n_steps=config_values.num_steps)
+        agent = PPO(PPOMlp, env, verbose=0, n_steps=config_values.num_steps, seed=env.training_config.seed)
 
     if config_values.session_type == "TRAINING":
         # We're in a training session
@@ -103,8 +100,19 @@ def run_stable_baselines3_ppo(
         # Default to being in an evaluation session
         print("Starting evaluation session...")
         _LOGGER.debug("Starting evaluation session...")
-        evaluate_policy(agent, env, n_eval_episodes=config_values.num_episodes)
 
+        for episode in range(0, config_values.num_episodes):
+            obs = env.reset()
+
+            for step in range(0, config_values.num_steps):
+                action, _states = agent.predict(
+                    obs,
+                    deterministic=env.training_config.deterministic
+                )
+                # convert to int if action is a numpy array
+                if isinstance(action, np.ndarray):
+                    action = np.int64(action)
+                obs, rewards, done, info = env.step(action)
     env.close()
 
 
@@ -138,7 +146,7 @@ def run_stable_baselines3_a2c(
             _LOGGER.error("Could not load agent")
             _LOGGER.error("Exception occured", exc_info=True)
     else:
-        agent = A2C("MlpPolicy", env, verbose=0, n_steps=config_values.num_steps)
+        agent = A2C("MlpPolicy", env, verbose=0, n_steps=config_values.num_steps, seed=env.training_config.seed)
 
     if config_values.session_type == "TRAINING":
         # We're in a training session
@@ -151,7 +159,18 @@ def run_stable_baselines3_a2c(
         # Default to being in an evaluation session
         print("Starting evaluation session...")
         _LOGGER.debug("Starting evaluation session...")
-        evaluate_policy(agent, env, n_eval_episodes=config_values.num_episodes)
+        for episode in range(0, config_values.num_episodes):
+            obs = env.reset()
+
+            for step in range(0, config_values.num_steps):
+                action, _states = agent.predict(
+                    obs,
+                    deterministic=env.training_config.deterministic
+                )
+                # convert to int if action is a numpy array
+                if isinstance(action, np.ndarray):
+                    action = np.int64(action)
+                obs, rewards, done, info = env.step(action)
 
     env.close()