Merge pull request #6 from rradules/item-gathering

Item gathering added to main

momadm_benchmarks/envs/beach_domain/beach_domain.py

@@ -251,7 +251,7 @@ def step(self, actions):
         # Obs: agent type, section id, section capacity, section consumption, % of agents of current type
         observations = {agent: None for agent in self.agents}
         # Note that agents only receive the reward after the last timestep
-        rewards = {self.agents[i]: np.array([0, 0], dtype=np.float32) for _ in range(self.num_agents)}
+        rewards = {self.agents[i]: np.array([0, 0], dtype=np.float32) for i in range(self.num_agents)}
 
         for i, agent in enumerate(self.agents):
             observations[agent] = self._get_obs(i, section_consumptions, section_agent_types)

momadm_benchmarks/envs/item_gathering/__init__.py

@@ -0,0 +1,13 @@
+"""Item Gathering environment.
+
+Johan Källström and Fredrik Heintz. 2019. Tunable Dynamics in Agent-Based Simulation using Multi-Objective
+Reinforcement Learning. Presented at the Adaptive and Learning Agents Workshop at AAMAS 2019.
+https://liu.diva-portal.org/smash/record.jsf?pid=diva2%3A1362933&dswid=9018
+
+Notes:
+    - In contrast to the original environment, the observation space is a 2D array of integers, i.e.,
+    the map of the environment, where each integer represents either agents (1 for the agent receiving the observation,
+     2 for the other agents) or items (3, 4, etc., depending on the number of items).
+    - The number of agents and items is configurable, by providing an initial map.
+    - If no initial map is provided, the environment uses a default map
+"""

momadm_benchmarks/envs/item_gathering/asset_utils.py

@@ -0,0 +1,54 @@
+"""disgracefully stolen.
+
+https://stackoverflow.com/questions/73956440/python-to-change-image-into-a-color-that-is-not-one-of-the-images-dominant-colo
+"""
+
+import os
+
+import numpy as np
+from PIL import Image
+
+
+def get_colored(asset: str, count: int):
+    """Generating random colored visual assets for dynamic environment variables.
+
+    Args:
+        asset: `str` value. Either "item" or "agent".
+        count: `int` value. The amount of total agents/items in the environment.
+
+    Returns:
+        numpy.ndarray with RGB values for the new colored assets.
+    """
+    assert asset == "item" or asset == "agent", "You must specify an asset to get colored versions of."
+
+    path = os.path.dirname(os.path.realpath(__file__))
+    seed = 1 if asset == "item" else 2
+    rng = np.random.default_rng(seed)  # local seed
+    im = Image.open(f"{path}/assets/{asset}.png").convert("RGBA")
+    alpha = im.getchannel("A")
+    im = im.convert("RGB")
+
+    for i in range(count - 1):  # subtracting the default asset
+        rgb = rng.random((3, 3))
+        matrix = (rgb[0, 0], rgb[1, 0], rgb[2, 0], 0, rgb[0, 1], rgb[1, 1], rgb[2, 1], 0, rgb[0, 2], rgb[1, 2], rgb[2, 2], 0)
+
+        # Apply above transform, reinsert alpha and save
+        colored_im = im.convert("RGB", matrix)
+        colored_im.putalpha(alpha)
+        colored_im.save(f"{path}/assets/colored/{asset}{i}.png")
+
+
+def del_colored(asset: str, count: int):
+    """Deleting the previously random colored visual assets.
+
+    Args:
+        asset: `str` value. Either "item" or "agent".
+        count: `int` value. The amount of total agents/items in the environment.
+
+    """
+    assert asset == "item" or asset == "agent", "You must specify an asset to delete colored versions of."
+
+    path = os.path.dirname(os.path.realpath(__file__))
+
+    for i in range(count - 1):  # subtracting the default asset
+        os.remove(f"{path}/assets/colored/{asset}{i}.png")