added MarlFrameStack and salina stuff

algorithms/utils.py

@@ -1,9 +1,11 @@
 import re
 import torch
+import numpy as np
 import yaml
 from pathlib import Path
 from salina import instantiate_class
 from salina import TAgent
+from salina.agents.gyma import AutoResetGymAgent, _torch_type, _format_frame
 
 
 def load_yaml_file(path: Path):
@@ -27,4 +29,67 @@ class CombineActionsAgent(TAgent):
         keys = list(self.workspace.keys())
         action_keys = sorted([k for k in keys if bool(re.match(self.pattern, k))])
         actions = torch.cat([self.get((k, t)) for k in action_keys], 0)
-        self.set((f'action', t), actions.unsqueeze(0))
+        actions = actions if len(action_keys) <= 1 else actions.unsqueeze(0)
+        self.set((f'action', t), actions)
+
+
+class AutoResetGymMultiAgent(AutoResetGymAgent):
+    AGENT_PREFIX = 'agent#'
+    REWARD       =  'reward'
+    CUMU_REWARD  = 'cumulated_reward'
+    SEP          = '_'
+
+    def __init__(self, *args, n_agents, **kwargs):
+        super(AutoResetGymMultiAgent, self).__init__(*args, **kwargs)
+        self.n_agents = n_agents
+
+    def prefix(self, agent_id, name):
+        return f'{self.AGENT_PREFIX}{agent_id}{self.SEP}{name}'
+
+    def _reset(self, k, save_render):
+        ret = super()._reset(k, save_render)
+        self.cumulated_reward[k] = [0.0]*self.n_agents
+        del ret['cumulated_reward']
+        cumu_rew = {self.prefix(agent_i, self.CUMU_REWARD): torch.zeros(1).float()
+                    for agent_i in range(self.n_agents)}
+        rewards  = {self.prefix(agent_i, self.REWARD)     : torch.zeros(1).float()
+                    for agent_i in range(self.n_agents)}
+        ret.update(cumu_rew)
+        ret.update(rewards)
+        return ret
+
+    def _step(self, k, action, save_render):
+        self.timestep[k] += 1
+        env = self.envs[k]
+        if len(action.size()) == 0:
+            action = action.item()
+            assert isinstance(action, int)
+        else:
+            action = np.array(action.tolist())
+        o, r, d, _ = env.step(action)
+        self.cumulated_reward[k] = [x+y for x, y in zip(r, self.cumulated_reward[k])]
+        print(o.shape)
+        observation = _format_frame(o)
+        if isinstance(observation, torch.Tensor):
+            print(observation.shape)
+            observation = {self.prefix(agent_i, 'env_obs'): observation[agent_i]
+                           for agent_i in range(self.n_agents)}
+            print(observation)
+        else:
+            assert isinstance(observation, dict)
+        if d:
+            self.is_running[k] = False
+
+        if save_render:
+            image = env.render(mode="image").unsqueeze(0)
+            observation["rendering"] = image
+        ret = {
+            **observation,
+            "done": torch.tensor([d]),
+            "initial_state": torch.tensor([False]),
+            "reward": torch.tensor(r).float(),
+            "timestep": torch.tensor([self.timestep[k]]),
+            "cumulated_reward": torch.tensor(self.cumulated_reward[k]).float(),
+        }
+        return _torch_type(ret)
+

environments/factory/__init__.py

@@ -1,4 +1,4 @@
-def make(env_name, n_agents=1, pomdp_r=2, max_steps=400, stack_n_frames=3):
+def make(env_name, pomdp_r=2, max_steps=400, stack_n_frames=3, n_agents=1,  individual_rewards=False):
     import yaml
     from pathlib import Path
     from environments.factory.combined_factories import DirtItemFactory
@@ -12,7 +12,8 @@ def make(env_name, n_agents=1, pomdp_r=2, max_steps=400, stack_n_frames=3):
     obs_props = ObservationProperties(render_agents=AgentRenderOptions.COMBINED,
                                       frames_to_stack=stack_n_frames, pomdp_r=pomdp_r)
 
-    factory_kwargs = dict(n_agents=n_agents, max_steps=max_steps, obs_prop=obs_props,
+    factory_kwargs = dict(n_agents=n_agents, individual_rewards=individual_rewards,
+                          max_steps=max_steps, obs_prop=obs_props,
                           mv_prop=MovementProperties(**dictionary['movement_props']),
                           dirt_prop=DirtProperties(**dictionary['dirt_props']),
                           record_episodes=False, verbose=False, **dictionary['factory_props']

environments/factory/base/base_factory.py

@@ -15,12 +15,11 @@ from environments.helpers import Constants as c, Constants
 from environments import helpers as h
 from environments.factory.base.objects import Agent, Tile, Action
 from environments.factory.base.registers import Actions, Entities, Agents, Doors, FloorTiles, WallTiles, PlaceHolders
-from environments.utility_classes import MovementProperties, ObservationProperties
+from environments.utility_classes import MovementProperties, ObservationProperties, MarlFrameStack
 from environments.utility_classes import AgentRenderOptions as a_obs
 
 import simplejson
 
-
 REC_TAC = 'rec_'
 
 
@@ -57,7 +56,7 @@ class BaseFactory(gym.Env):
 
     def __enter__(self):
         return self if self.obs_prop.frames_to_stack == 0 else \
-            FrameStack(self, self.obs_prop.frames_to_stack)
+            MarlFrameStack(FrameStack(self, self.obs_prop.frames_to_stack))
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         self.close()

environments/utility_classes.py

@@ -1,5 +1,6 @@
-from enum import Enum
 from typing import NamedTuple, Union
+import gym
+from gym.wrappers.frame_stack import FrameStack
 
 
 class AgentRenderOptions(object):
@@ -22,3 +23,14 @@ class ObservationProperties(NamedTuple):
     cast_shadows = True
     frames_to_stack: int = 0
     pomdp_r: int = 0
+
+
+class MarlFrameStack(gym.ObservationWrapper):
+    def __init__(self, env):
+        super().__init__(env)
+
+    def observation(self, observation):
+        if isinstance(self.env, FrameStack) and self.env.unwrapped.n_agents > 1:
+            return observation[0:].swapaxes(0, 1)
+        return observation
+

studies/sat_mad.py

@@ -9,7 +9,12 @@ from pathlib import Path
 import numpy as np
 from tqdm import tqdm
 import time
-from algorithms.utils import add_env_props, load_yaml_file, CombineActionsAgent
+from algorithms.utils import (
+    add_env_props,
+    load_yaml_file,
+    CombineActionsAgent,
+    AutoResetGymMultiAgent
+)
 
 
 class A2CAgent(TAgent):
@@ -32,8 +37,8 @@ class A2CAgent(TAgent):
 
     def get_obs(self, t):
         observation = self.get(("env/env_obs", t))
+        print(observation.shape)
         if self.marl:
-            observation = observation.permute(2, 0, 1, 3, 4, 5)
             observation = observation[self.agent_id]
         return observation
 
@@ -57,7 +62,7 @@ if __name__ == '__main__':
     # Setup workspace
     uid = time.time()
     workspace = Workspace()
-    n_agents = 1
+    n_agents = 2
 
     # load config
     cfg = load_yaml_file(Path(__file__).parent / 'sat_mad.yaml')
@@ -65,10 +70,11 @@ if __name__ == '__main__':
     cfg['env'].update({'n_agents': n_agents})
 
     # instantiate agent and env
-    env_agent = AutoResetGymAgent(
+    env_agent = AutoResetGymMultiAgent(
         get_class(cfg['env']),
         get_arguments(cfg['env']),
-        n_envs=1
+        n_envs=1,
+        n_agents=n_agents
     )
 
     a2c_agents = [instantiate_class({**cfg['agent'],
@@ -103,7 +109,8 @@ if __name__ == '__main__':
                     f'agent{agent_id}_action_probs', "env/reward",
                     f"agent{agent_id}_action"
                 ]
-                td = gae(critic, reward, done, 0.99, 0.3)
+                reward = reward[agent_id]
+                td = gae(critic, reward, done, 0.98, 0.25)
                 td_error = td ** 2
                 critic_loss = td_error.mean()
                 entropy_loss = Categorical(action_probs).entropy().mean()
@@ -118,11 +125,12 @@ if __name__ == '__main__':
                 optimizer = optimizers[agent_id]
                 optimizer.zero_grad()
                 loss.backward()
-                #torch.nn.utils.clip_grad_norm_(a2c_agents[agent_id].parameters(), 2)
+                #torch.nn.utils.clip_grad_norm_(a2c_agents[agent_id].parameters(), .5)
                 optimizer.step()
 
                 # Compute the cumulated reward on final_state
-                creward = workspace["env/cumulated_reward"]
+                creward = workspace["env/cumulated_reward"]#[agent_id].unsqueeze(-1)
+                print(creward.shape, done.shape)
                 creward = creward[done]
                 if creward.size()[0] > 0:
                     cum_r = creward.mean().item()

studies/sat_mad.yaml

@@ -11,15 +11,16 @@ env:
   pomdp_r:            2
   max_steps:          400
   stack_n_frames:     3
+  individual_rewards: True
 
 algorithm:
   max_epochs:             1000000
   n_envs:                 1
-  n_timesteps:            16
+  n_timesteps:            10
   discount_factor:        0.99
   entropy_coef:           0.01
   critic_coef:            1.0
-  gae:                    0.3
+  gae:                    0.25
   optimizer:
     classname:            torch.optim.Adam
     lr:                   0.0003