add monthly normalized env

src/rl4greencrab/__init__.py

@@ -1,6 +1,7 @@
 from rl4greencrab.envs.green_crab_ipm import greenCrabEnv, greenCrabSimplifiedEnv
 from rl4greencrab.envs.time_series import timeSeriesEnv
 from rl4greencrab.envs.green_crab_monthly_env import greenCrabMonthEnv
+from rl4greencrab.envs.green_crab_monthly_env_norm import greenCrabMonthEnvNormalized
 from rl4greencrab.agents.const_action import constAction, constActionNatUnits, multiConstAction
 from rl4greencrab.agents.const_escapement import constEsc
 from rl4greencrab.utils.simulate import simulator, get_simulator, evaluate_agent
@@ -21,4 +22,8 @@
 register(
     id="monthenv", 
     entry_point="rl4greencrab.envs.green_crab_monthly_env:greenCrabMonthEnv",
+)
+register(
+    id="monthenvnorm", 
+    entry_point="rl4greencrab.envs.green_crab_monthly_env_norm:greenCrabMonthEnvNormalized",
 )

src/rl4greencrab/envs/green_crab_monthly_env_norm.py

@@ -0,0 +1,52 @@
+import gymnasium as gym
+import logging
+import numpy as np
+
+from gymnasium import spaces
+from scipy.stats import norm
+from rl4greencrab.envs.green_crab_monthly_env import greenCrabMonthEnv
+
+logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO)
+
+class greenCrabMonthEnvNormalized(greenCrabMonthEnv):
+    def __init__(self, config={}):
+        super().__init__(config=config)
+        self.observation_space = spaces.Box(
+            np.array([-1], dtype=np.float32),
+            np.array([1], dtype=np.float32),
+            dtype=np.float32,
+        )
+        self.action_space = spaces.Box(
+            np.float32([-1, -1, -1]),
+            np.float32([1, 1, 1]),
+            dtype=np.float32,
+        )
+        self.max_action = config.get('max_action', 2000) # ad hoc based on previous values
+        self.cpue_normalization = config.get('cpue_normalization', 100)
+
+    def step(self, action):
+        action_natural_units = np.maximum(self.max_action * (1 + action)/2 , 0.) #convert to normal action
+        obs, rew, term, trunc, info = super().step(
+            np.float32(action_natural_units)
+        )
+        normalized_cpue = 2 * self.cpue_2(obs, action_natural_units) - 1
+        # observation = np.float32(np.append(normalized_cpue, action))
+        observation = normalized_cpue
+        rew = 10 * rew # use larger rewards, possibly makes trainer easier?
+        return observation, rew, term, trunc, info
+
+    def reset(self, *, seed=42, options=None):
+        _, info = super().reset(seed=seed, options=options)
+
+        # completely new  obs
+        return - np.ones(shape=self.observation_space.shape, dtype=np.float32), info
+
+    def cpue_2(self, obs, action_natural_units):
+        # If you don't set traps, the catch-per-effort is 0/0.  Should be NaN, but we call it 0
+        if np.sum(action_natural_units) <= 0:
+            return np.float32([0])
+        # can't tell which traps caught each number of crabs here. Perhaps too simple but maybe realistic 
+        cpue_2 = np.float32([
+            np.sum(obs[0]) / (self.cpue_normalization * np.sum(action_natural_units)),
+        ])
+        return cpue_2