Merge pull request #21 from instadeepai/feat/pixel-obs-vaults

Add support for pixel-obs environments

Dockerfile

@@ -2,6 +2,7 @@ FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04
 
 # Ensure no installs try to launch interactive screen
 ARG DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
 
 # Update packages and install python3.9 and other dependencies
 RUN apt-get update -y && \
@@ -33,14 +34,18 @@ RUN pip install --quiet --upgrade pip setuptools wheel &&  \
     pip install -e . && \
     pip install flashbax==0.1.0
 
-ENV SC2PATH /home/app/StarCraftII
+# ENV SC2PATH /home/app/StarCraftII
 # RUN ./install_environments/smacv1.sh
-RUN ./install_environments/smacv2.sh
+# RUN ./install_environments/smacv2.sh
 
 # ENV LD_LIBRARY_PATH $LD_LIBRARY_PATH:/root/.mujoco/mujoco210/bin:/usr/lib/nvidia
 # ENV SUPPRESS_GR_PROMPT 1
 # RUN ./install_environments/mamujoco.sh
 
+RUN ./install_environments/pettingzoo.sh
+
+# RUN ./install_environments/flatland.sh
+
 # Copy all code
 COPY ./examples ./examples
 COPY ./baselines ./baselines

README.md

@@ -104,12 +104,12 @@ We are in the process of migrating our datasets from TF Records to Flashbax Vaul
 | 💣SMAC v2 | terran_5_vs_5 <br/> zerg_5_vs_5 <br/> terran_10_vs_10 | 5 <br/> 5 <br/> 10 | Discrete | Vector | Dense | Heterog | [source](https://github.com/oxwhirl/smacv2) |
 | 🚅Flatland | 3 Trains  <br/> 5 Trains | 3 <br/> 5 | Discrete | Vector | Sparse | Homog | [source](https://flatland.aicrowd.com/intro.html) |
 | 🐜MAMuJoCo | 2-HalfCheetah <br/> 2-Ant <br/> 4-Ant | 2 <br/> 2 <br/> 4 | Cont. | Vector | Dense | Heterog <br/> Homog <br/> Homog | [source](https://github.com/schroederdewitt/multiagent_mujoco) |
-
+| 🐻PettingZoo | Pursuit  <br/> Co-op Pong | 8 <br/> 2 | Discrete <br/> Discrete  | Pixels <br/> Pixels | Dense | Homog <br/> Heterog | [source](https://pettingzoo.farama.org/) |
 
 ### Legacy Datasets (still to be migrated to Vault) 👴
 | Environment | Scenario | Agents | Act | Obs | Reward | Types | Repo |
 |-----|----|----|-----|-----|----|----|-----|
-| 🐻PettingZoo | Pursuit  <br/> Co-op Pong <br/> PistonBall <br/> KAZ| 8 <br/> 2 <br/> 15 <br/> 2| Discrete <br/> Discrete <br/> Cont. <br/> Discrete | Pixels <br/> Pixels <br/> Pixels <br/> Vector | Dense | Homog <br/> Heterog <br/> Homog <br/> Heterog| [source](https://pettingzoo.farama.org/) |
+| 🐻PettingZoo | PistonBall <br/> KAZ| 15 <br/> 2|  Cont. <br/> Discrete | Pixels <br/> Vector | Dense | Homog <br/> Heterog| [source](https://pettingzoo.farama.org/) |
 | 🏙️CityLearn | 2022_all_phases | 17 | Cont. | Vector | Dense | Homog | [source](https://github.com/intelligent-environments-lab/CityLearn) |
 | 🔌Voltage Control | case33_3min_final | 6 | Cont. | Vector | Dense | Homog | [source](https://github.com/Future-Power-Networks/MAPDN) |
 | 🔴MPE | simple_adversary | 3 | Discrete. | Vector | Dense | Competitive | [source](https://pettingzoo.farama.org/environments/mpe/simple_adversary/) |

baselines/main.py

@@ -13,20 +13,21 @@
 # limitations under the License.
 from absl import app, flags
 
-from og_marl.environments.utils import get_environment
+from og_marl.environments import get_environment
 from og_marl.loggers import JsonWriter, WandbLogger
 from og_marl.offline_dataset import download_and_unzip_vault
 from og_marl.replay_buffers import FlashbaxReplayBuffer
+from og_marl.tf2.networks import CNNEmbeddingNetwork
 from og_marl.tf2.systems import get_system
 from og_marl.tf2.utils import set_growing_gpu_memory
 
 set_growing_gpu_memory()
 
 FLAGS = flags.FLAGS
-flags.DEFINE_string("env", "smac_v1", "Environment name.")
-flags.DEFINE_string("scenario", "3m", "Environment scenario name.")
+flags.DEFINE_string("env", "pettingzoo", "Environment name.")
+flags.DEFINE_string("scenario", "pursuit", "Environment scenario name.")
 flags.DEFINE_string("dataset", "Good", "Dataset type.: 'Good', 'Medium', 'Poor' or 'Replay' ")
-flags.DEFINE_string("system", "dbc", "System name.")
+flags.DEFINE_string("system", "qmix", "System name.")
 flags.DEFINE_integer("seed", 42, "Seed.")
 flags.DEFINE_float("trainer_steps", 5e4, "Number of training steps.")
 flags.DEFINE_integer("batch_size", 64, "Number of training steps.")
@@ -43,7 +44,7 @@ def main(_):
 
     env = get_environment(FLAGS.env, FLAGS.scenario)
 
-    buffer = FlashbaxReplayBuffer(sequence_length=20, sample_period=2)
+    buffer = FlashbaxReplayBuffer(sequence_length=20, sample_period=1)
 
     download_and_unzip_vault(FLAGS.env, FLAGS.scenario)
 
@@ -65,6 +66,9 @@ def main(_):
     )
 
     system_kwargs = {"add_agent_id_to_obs": True}
+    if FLAGS.scenario == "pursuit":
+        system_kwargs["observation_embedding_network"] = CNNEmbeddingNetwork()
+
     system = get_system(FLAGS.system, env, logger, **system_kwargs)
 
     system.train_offline(buffer, max_trainer_steps=FLAGS.trainer_steps, json_writer=json_writer)

examples/tf2/run_all_baselines.py

@@ -1,32 +1,45 @@
 import os
 
+# import module
+import traceback
+
 from og_marl.environments import get_environment
-from og_marl.loggers import JsonWriter, WandbLogger
+from og_marl.loggers import TerminalLogger, JsonWriter
 from og_marl.replay_buffers import FlashbaxReplayBuffer
+from og_marl.tf2.networks import CNNEmbeddingNetwork
 from og_marl.tf2.systems import get_system
 from og_marl.tf2.utils import set_growing_gpu_memory
 
 set_growing_gpu_memory()
 
-os.environ["SUPPRESS_GR_PROMPT"] = 1
+# For MAMuJoCo
+os.environ["SUPPRESS_GR_PROMPT"] = "1"
 
 scenario_system_configs = {
     "smac_v1": {
         "3m": {
-            "systems": ["idrqn", "idrqn+cql", "idrqn+bcq", "qmix+cql", "qmix+bcq", "maicq"],
+            "systems": ["idrqn", "idrqn+cql", "idrqn+bcq", "qmix+cql", "qmix+bcq", "maicq", "dbc"],
             "datasets": ["Good"],
-            "trainer_steps": 3000,
+            "trainer_steps": 2000,
             "evaluate_every": 1000,
         },
     },
-    "mamujoco": {
-        "2halfcheetah": {
-            "systems": ["iddpg", "iddpg+cql", "maddpg+cql", "maddpg", "omar"],
+    "pettingzoo": {
+        "pursuit": {
+            "systems": ["idrqn", "idrqn+cql", "idrqn+bcq", "qmix+cql", "qmix+bcq", "maicq", "dbc"],
             "datasets": ["Good"],
-            "trainer_steps": 3000,
+            "trainer_steps": 2000,
             "evaluate_every": 1000,
         },
     },
+    # "mamujoco": {
+    #     "2halfcheetah": {
+    #         "systems": ["iddpg", "iddpg+cql", "maddpg+cql", "maddpg", "omar"],
+    #         "datasets": ["Good"],
+    #         "trainer_steps": 3000,
+    #         "evaluate_every": 1000,
+    #     },
+    # },
 }
 
 seeds = [42]
@@ -44,7 +57,7 @@
                             "system": env_name,
                             "seed": seed,
                         }
-                        logger = WandbLogger(config, project="og-marl-baselines")
+                        logger = TerminalLogger()
                         env = get_environment(env_name, scenario_name)
 
                         buffer = FlashbaxReplayBuffer(sequence_length=20, sample_period=1)
@@ -55,10 +68,18 @@
                             raise ValueError("Vault not found. Exiting.")
 
                         json_writer = JsonWriter(
-                            "logs", system_name, f"{scenario_name}_{dataset_name}", env_name, seed
+                            "test_all_baselines",
+                            system_name,
+                            f"{scenario_name}_{dataset_name}",
+                            env_name,
+                            seed,
                         )
 
                         system_kwargs = {"add_agent_id_to_obs": True}
+
+                        if scenario_name == "pursuit":
+                            system_kwargs["observation_embedding_network"] = CNNEmbeddingNetwork()
+
                         system = get_system(system_name, env, logger, **system_kwargs)
 
                         trainer_steps = scenario_system_configs[env_name][scenario_name][
@@ -75,7 +96,7 @@
                         )
                     except:  # noqa: E722
                         logger.close()
-                        print()
-                        print("BROKEN")
+                        print("BROKEN:", env_name, scenario_name, system_name)
+                        traceback.print_exc()
                         print()
                         continue

install_environments/requirements/pettingzoo.txt

@@ -2,7 +2,7 @@ autorom
 gym
 numpy
 opencv-python
-pettingzoo==1.22.0
+pettingzoo==1.23.1
 pygame
 pymunk
 scipy

mkdocs.yml

@@ -38,7 +38,6 @@ theme:
 
 nav:
   - Home: 'index.md'
-  - Datasets: 'datasets.md'
   - Baseline Results: 'baselines.md'
   - Updates: 'updates.md'
   - API Reference: 'api.md'

og_marl/environments/__init__.py

@@ -18,7 +18,7 @@
 from og_marl.environments.base import BaseEnvironment
 
 
-def get_environment(env_name: str, scenario: str) -> BaseEnvironment:
+def get_environment(env_name: str, scenario: str) -> BaseEnvironment:  # noqa: C901
     if env_name == "smac_v1":
         from og_marl.environments.smacv1 import SMACv1
 
@@ -31,6 +31,14 @@ def get_environment(env_name: str, scenario: str) -> BaseEnvironment:
         from og_marl.environments.old_mamujoco import MAMuJoCo
 
         return MAMuJoCo(scenario)
+    elif scenario == "pursuit":
+        from og_marl.environments.pursuit import Pursuit
+
+        return Pursuit()
+    elif scenario == "coop_pong":
+        from og_marl.environments.coop_pong import CooperativePong
+
+        return CooperativePong()
     elif env_name == "gymnasium_mamujoco":
         from og_marl.environments.gymnasium_mamujoco import MAMuJoCo
 

og_marl/environments/coop_pong.py

@@ -0,0 +1,114 @@
+# python3
+# Copyright 2021 InstaDeep Ltd. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Wrapper for Cooperative Pettingzoo environments."""
+from typing import Any, List, Dict
+
+import numpy as np
+from pettingzoo.butterfly import cooperative_pong_v5
+import supersuit
+
+from og_marl.environments.base import BaseEnvironment
+from og_marl.environments.base import Observations, ResetReturn, StepReturn
+
+
+class CooperativePong(BaseEnvironment):
+    """Environment wrapper PettingZoo Cooperative Pong."""
+
+    def __init__(
+        self,
+    ) -> None:
+        """Constructor."""
+        self._environment = cooperative_pong_v5.parallel_env(render_mode="rgb_array")
+        # Wrap environment with supersuit pre-process wrappers
+        self._environment = supersuit.color_reduction_v0(self._environment, mode="R")
+        self._environment = supersuit.resize_v0(self._environment, x_size=145, y_size=84)
+        self._environment = supersuit.dtype_v0(self._environment, dtype="float32")
+        self._environment = supersuit.normalize_obs_v0(self._environment)
+
+        self._agents = self._environment.possible_agents
+        self._done = False
+        self.max_episode_length = 900
+
+    def reset(self) -> ResetReturn:
+        """Resets the env."""
+        # Reset the environment
+        observations, _ = self._environment.reset()  # type: ignore
+
+        # Convert observations
+        observations = self._convert_observations(observations)
+
+        # Global state
+        env_state = self._create_state_representation(observations, first=True)
+
+        # Infos
+        info = {"state": env_state, "legals": self._legals}
+
+        return observations, info
+
+    def step(self, actions: Dict[str, np.ndarray]) -> StepReturn:
+        """Steps in env."""
+        # Step the environment
+        observations, rewards, terminals, truncations, _ = self._environment.step(actions)
+
+        # Convert observations
+        observations = self._convert_observations(observations)
+
+        # Global state
+        env_state = self._create_state_representation(observations)
+
+        # Extra infos
+        info = {"state": env_state, "legals": self._legals}
+
+        return observations, rewards, terminals, truncations, info
+
+    def _create_state_representation(self, observations: Observations, first: bool = False) -> Any:
+        if first:
+            self._state_history = np.zeros((84, 145, 4), "float32")
+
+        state = np.expand_dims(observations["paddle_0"][:, :], axis=-1)
+
+        # framestacking
+        self._state_history = np.concatenate((state, self._state_history[:, :, :3]), axis=-1)
+
+        return self._state_history
+
+    def _convert_observations(self, observations: List) -> Observations:
+        """Make observations partial."""
+        processed_observations = {}
+        for agent in self._agents:
+            if agent == "paddle_0":
+                agent_obs = observations[agent][:, :110]  # hide the other agent
+            else:
+                agent_obs = observations[agent][:, 35:]  # hide the other agent
+
+            agent_obs = np.expand_dims(agent_obs, axis=-1)
+            processed_observations[agent] = agent_obs
+
+        return processed_observations
+
+    def __getattr__(self, name: str) -> Any:
+        """Expose any other attributes of the underlying environment.
+
+        Args:
+            name (str): attribute.
+
+        Returns:
+            Any: return attribute from env or underlying env.
+        """
+        if hasattr(self.__class__, name):
+            return self.__getattribute__(name)
+        else:
+            return getattr(self._environment, name)

og_marl/environments/pettingzoo_base.py

@@ -30,7 +30,7 @@ def __init__(self) -> None:
     def reset(self) -> ResetReturn:
         """Resets the env."""
         # Reset the environment
-        observations = self._environment.reset()  # type: ignore
+        observations, _ = self._environment.reset()  # type: ignore
 
         # Global state
         env_state = self._create_state_representation(observations)