first icm steps

algorithms/common.py

@@ -24,7 +24,7 @@ class BaseLearner:
         self.n_agents = n_agents
         self.n_grad_steps = n_grad_steps
         self.train_every = train_every
-        self.stack_n_frames = deque(stack_n_frames)
+        self.stack_n_frames = deque(maxlen=stack_n_frames)
         self.device = 'cpu'
         self.n_updates = 0
         self.step = 0
@@ -51,6 +51,9 @@ class BaseLearner:
     def on_episode_end(self, n_steps):
         pass
 
+    def on_all_done(self):
+        pass
+
     def train(self):
         pass
 
@@ -93,6 +96,7 @@ class BaseLearner:
                         f' eps: {self.eps:.4f}\tRunning loss: {sum(list(self.running_loss)) / len(self.running_loss):.4f}\tUpdates:{self.n_updates}')
             except Exception as e:
                 pass
+        self.on_all_done()
 
 
 class BaseBuffer:
@@ -180,18 +184,17 @@ class BaseDDQN(BaseDQN):
         return values + (advantages - advantages.mean())
 
 
-class QTRANtestNet(nn.Module):
-    def __init__(self, backbone_dims=[3*5*5, 64, 64], q_head=[64, 9]):
-        super(QTRANtestNet, self).__init__()
-        self.backbone = mlp_maker(backbone_dims, flatten=True, activation_last='elu')
-        self.q_head = mlp_maker(q_head)
+class BaseICM(nn.Module):
+    def __init__(self, backbone_dims=[2*3*5*5, 64, 64], head_dims=[2*64, 64, 9]):
+        super(BaseICM, self).__init__()
+        self.backbone = mlp_maker(backbone_dims, flatten=True)
+        self.icm = mlp_maker(head_dims)
+        self.ce = nn.CrossEntropyLoss()
 
-    def forward(self, x):
-        features = self.backbone(x)
-        qs = self.q_head(features)
-        return qs, features
-
-    @torch.no_grad()
-    def act(self, x) -> np.ndarray:
-        action = self.forward(x)[0].max(-1)[1].numpy()
-        return action
+    def forward(self, s0, s1, a):
+        phi_s0 = self.backbone(s0)
+        phi_s1 = self.backbone(s1)
+        cat = torch.cat((phi_s0, phi_s1), dim=1)
+        a_prime = torch.softmax(self.icm(cat), dim=-1)
+        ce = self.ce(a_prime, a)
+        return dict(prediction=a_prime, loss=ce)

algorithms/m_q_learner.py

@@ -51,3 +51,21 @@ class MQLearner(QLearner):
             # Compute loss
             loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - m_q_target, 2))
             self._backprop_loss(loss)
+
+from tqdm import trange
+class MQICMLearner(MQLearner):
+    def __init__(self, *args, icm, **kwargs):
+        super(MQICMLearner, self).__init__(*args, **kwargs)
+        self.icm = icm
+        self.icm_optimizer = torch.optim.Adam(self.icm.parameters())
+
+    def on_all_done(self):
+        for b in trange(50000):
+            batch = self.buffer.sample(128, 0)
+            s0, s1, a = batch.observation,  batch.next_observation, batch.action
+            loss = self.icm(s0, s1, a.squeeze())['loss']
+            self.icm_optimizer.zero_grad()
+            loss.backward()
+            self.icm_optimizer.step()
+            if b%100 == 0:
+                print(loss.item())

algorithms/q_learner.py

@@ -100,32 +100,28 @@ class QLearner(BaseLearner):
 
 if __name__ == '__main__':
     from environments.factory.simple_factory import SimpleFactory, DirtProperties, MovementProperties
-    from algorithms.common import BaseDDQN
-    from algorithms.m_q_learner import MQLearner
+    from algorithms.common import BaseDDQN, BaseICM
+    from algorithms.m_q_learner import MQLearner, MQICMLearner
     from algorithms.vdn_learner import VDNLearner
-    from algorithms.udr_learner import UDRLearner
 
     N_AGENTS = 1
 
-    dirt_props = DirtProperties(clean_amount=1, gain_amount=0.1, max_global_amount=20,
-                                max_local_amount=1, spawn_frequency=5, max_spawn_ratio=0.05,
-                                dirt_smear_amount=0.0)
-    move_props = MovementProperties(allow_diagonal_movement=True,
-                                    allow_square_movement=True,
-                                    allow_no_op=False)
-
-    env = SimpleFactory(n_agents=1, dirt_properties=dirt_props, pomdp_radius=2, max_steps=400, parse_doors=False,
-                        movement_properties=move_props, level_name='rooms', frames_to_stack=0,
-                        omit_agent_slice_in_obs=True, combin_agent_slices_in_obs=True, record_episodes=False
-                    )
+    with (Path(f'../environments/factory/env_default_param.yaml')).open('r') as f:
+        env_kwargs = yaml.load(f, Loader=yaml.FullLoader)
 
+    env = SimpleFactory(**env_kwargs)
     obs_shape = np.prod(env.observation_space.shape)
     n_actions = env.action_space.n
 
-    dqn, target_dqn = BaseDDQN(backbone_dims=[obs_shape, 128, 128], advantage_dims=[128, n_actions], value_dims=[128,1], activation='leaky_relu'),\
-                      BaseDDQN(backbone_dims=[obs_shape, 128, 128], advantage_dims=[128, n_actions], value_dims=[128,1], activation='leaky_relu')
+    dqn, target_dqn = BaseDDQN(backbone_dims=[obs_shape, 128, 128], advantage_dims=[128, n_actions], value_dims=[128, 1], activation='leaky_relu'),\
+                      BaseDDQN(backbone_dims=[obs_shape, 128, 128], advantage_dims=[128, n_actions], value_dims=[128, 1], activation='leaky_relu')
 
-    learner = MQLearner(dqn, target_dqn, env, 50000, target_update=5000, lr=0.0007, gamma=0.99, n_agents=N_AGENTS, tau=0.95, max_grad_norm=10,
-                        train_every=('step', 4), eps_end=0.025, n_grad_steps=1, reg_weight=0.1, exploration_fraction=0.25, batch_size=64, weight_decay=1e-3)
+    icm = BaseICM(backbone_dims=[obs_shape, 64, 32], head_dims=[2*32, 64, n_actions])
+
+    learner = MQICMLearner(dqn, target_dqn, env, 50000, icm=icm,
+                           target_update=5000, lr=0.0007, gamma=0.99, n_agents=N_AGENTS, tau=0.95, max_grad_norm=10,
+                           train_every=('step', 4), eps_end=0.025, n_grad_steps=1, reg_weight=0.1, exploration_fraction=0.25,
+                           batch_size=64, weight_decay=1e-3
+                           )
     #learner.save(Path(__file__).parent / 'test' / 'testexperiment1337')
     learner.learn(100000)

environments/factory/renderer.py

@@ -15,7 +15,7 @@ class Entity(NamedTuple):
     value_operation: str = 'none'
     state: str = None
     id: int = 0
-    aux:Any=None
+    aux: Any = None
 
 
 class Renderer:

studies/sat_mad.py

@@ -0,0 +1,9 @@
+import numpy as np
+
+
+class SatMad(object):
+    def __init__(self):
+        pass
+
+if __name__ == '__main__':
+    pass