refactored algorithms

algorithms/common.py

@@ -0,0 +1,102 @@
+from typing import NamedTuple, Union
+from collections import deque, OrderedDict
+import numpy as np
+import random
+import torch
+import torch.nn as nn
+
+
+class BaseLearner:
+    def __init__(self, env, n_agents, lr):
+        self.env = env
+        self.n_agents = n_agents
+        self.lr = lr
+        self.device = 'cpu'
+
+    def to(self, device):
+        self.device = device
+        for attr, value in self.__dict__.items():
+            if isinstance(value, nn.Module):
+                value = value.to(self.device)
+        return self
+
+
+class Experience(NamedTuple):
+    # can be use for a single (s_t, a, r s_{t+1}) tuple
+    # or for a batch of tuples
+    observation:      np.ndarray
+    next_observation: np.ndarray
+    action:           np.ndarray
+    reward:           Union[float, np.ndarray]
+    done  :           Union[bool, np.ndarray]
+
+
+class BaseBuffer:
+    def __init__(self, size: int):
+        self.size = size
+        self.experience = deque(maxlen=size)
+
+    def __len__(self):
+        return len(self.experience)
+
+    def add(self, experience):
+        self.experience.append(experience)
+
+    def sample(self, k, cer=4):
+        sample = random.choices(self.experience, k=k-cer)
+        for i in range(cer): sample += [self.experience[-i]]
+        observations = torch.stack([torch.from_numpy(e.observation) for e in sample], 0).float()
+        next_observations = torch.stack([torch.from_numpy(e.next_observation) for e in sample], 0).float()
+        actions = torch.tensor([e.action for e in sample]).long()
+        rewards = torch.tensor([e.reward for e in sample]).float().view(-1, 1)
+        dones = torch.tensor([e.done for e in sample]).float().view(-1, 1)
+        return Experience(observations, next_observations, actions, rewards, dones)
+
+
+def soft_update(local_model, target_model, tau):
+    # taken from https://github.com/BY571/Munchausen-RL/blob/master/M-DQN.ipynb
+    for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
+        target_param.data.copy_(tau*local_param.data + (1.-tau)*target_param.data)
+
+
+def mlp_maker(dims, flatten=False, activation='elu', activation_last='identity'):
+    activations = {'elu': nn.ELU, 'relu': nn.ReLU,
+                  'leaky_relu': nn.LeakyReLU, 'tanh': nn.Tanh,
+                  'gelu': nn.GELU, 'identity': nn.Identity}
+    layers = [('Flatten', nn.Flatten())] if flatten else []
+    for i in range(1, len(dims)):
+        layers.append((f'Layer #{i - 1}: Linear', nn.Linear(dims[i - 1], dims[i])))
+        activation_str = activation if i != len(dims)-1 else activation_last
+        layers.append((f'Layer #{i - 1}: {activation_str.capitalize()}', activations[activation_str]()))
+    return nn.Sequential(OrderedDict(layers))
+
+
+class BaseDQN(nn.Module):
+    def __init__(self, dims=[3*5*5, 64, 64, 9]):
+        super(BaseDQN, self).__init__()
+        self.net = mlp_maker(dims, flatten=True)
+
+    @torch.no_grad()
+    def act(self, x) -> np.ndarray:
+        action = self.forward(x).max(-1)[1].numpy()
+        return action
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class BaseDDQN(BaseDQN):
+    def __init__(self,
+                 backbone_dims=[3*5*5, 64, 64],
+                 value_dims=[64, 1],
+                 advantage_dims=[64, 9]):
+        super(BaseDDQN, self).__init__(backbone_dims)
+        self.net = mlp_maker(backbone_dims, flatten=True)
+        self.value_head         =  mlp_maker(value_dims)
+        self.advantage_head     =  mlp_maker(advantage_dims)
+
+    def forward(self, x):
+        features = self.net(x)
+        advantages = self.advantage_head(features)
+        values = self.value_head(features)
+        return values + (advantages - advantages.mean())

algorithms/dqn.py

@@ -1,277 +0,0 @@
-from typing import NamedTuple, Union
-from collections import deque, OrderedDict
-import numpy as np
-import random
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class Experience(NamedTuple):
-    # can be use for a single (s_t, a, r s_{t+1}) tuple
-    # or for a batch of tuples
-    observation:      np.ndarray
-    next_observation: np.ndarray
-    action:           np.ndarray
-    reward:           Union[float, np.ndarray]
-    done  :           Union[bool, np.ndarray]
-
-
-class BaseBuffer:
-    def __init__(self, size: int):
-        self.size = size
-        self.experience = deque(maxlen=size)
-
-    def __len__(self):
-        return len(self.experience)
-
-    def add(self, experience):
-        self.experience.append(experience)
-
-    def sample(self, k, cer=4):
-        sample = random.choices(self.experience, k=k-cer)
-        for i in range(cer): sample += [self.experience[-i]]
-        observations = torch.stack([torch.from_numpy(e.observation) for e in sample], 0).float()
-        next_observations = torch.stack([torch.from_numpy(e.next_observation) for e in sample], 0).float()
-        actions = torch.tensor([e.action for e in sample]).long()
-        rewards = torch.tensor([e.reward for e in sample]).float().view(-1, 1)
-        dones = torch.tensor([e.done for e in sample]).float().view(-1, 1)
-        return Experience(observations, next_observations, actions, rewards, dones)
-
-
-
-
-def soft_update(local_model, target_model, tau):
-    # taken from https://github.com/BY571/Munchausen-RL/blob/master/M-DQN.ipynb
-    for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
-        target_param.data.copy_(tau*local_param.data + (1.-tau)*target_param.data)
-
-
-def mlp_maker(dims, flatten=False, activation='elu', activation_last='identity'):
-    activations = {'elu': nn.ELU, 'relu': nn.ReLU,
-                  'leaky_relu': nn.LeakyReLU, 'tanh': nn.Tanh,
-                  'gelu': nn.GELU, 'identity': nn.Identity}
-    layers = [('Flatten', nn.Flatten())] if flatten else []
-    for i in range(1, len(dims)):
-        layers.append((f'Layer #{i - 1}: Linear', nn.Linear(dims[i - 1], dims[i])))
-        activation_str = activation if i != len(dims)-1 else activation_last
-        layers.append((f'Layer #{i - 1}: {activation_str.capitalize()}', activations[activation_str]()))
-    return nn.Sequential(OrderedDict(layers))
-
-
-class BaseDQN(nn.Module):
-    def __init__(self, dims=[3*5*5, 64, 64, 9]):
-        super(BaseDQN, self).__init__()
-        self.net = mlp_maker(dims, flatten=True)
-
-    def act(self, x) -> np.ndarray:
-        with torch.no_grad():
-            action = self.forward(x).max(-1)[1].numpy()
-        return action
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class BaseDDQN(BaseDQN):
-    def __init__(self,
-                 backbone_dims=[3*5*5, 64, 64],
-                 value_dims=[64,1],
-                 advantage_dims=[64,9]):
-        super(BaseDDQN, self).__init__(backbone_dims)
-        self.net = mlp_maker(backbone_dims, flatten=True)
-        self.value_head         =  mlp_maker(value_dims)
-        self.advantage_head     =  mlp_maker(advantage_dims)
-
-    def forward(self, x):
-        features = self.net(x)
-        advantages = self.advantage_head(features)
-        values = self.value_head(features)
-        return values + (advantages - advantages.mean())
-
-
-class BaseQlearner:
-    def __init__(self, q_net, target_q_net, env, buffer_size, target_update, eps_end, n_agents=1,
-                 gamma=0.99, train_every_n_steps=4, n_grad_steps=1, tau=1.0, max_grad_norm=10,
-                 exploration_fraction=0.2, batch_size=64, lr=1e-4, reg_weight=0.0):
-        self.q_net = q_net
-        self.target_q_net = target_q_net
-        self.target_q_net.eval()
-        soft_update(self.q_net, self.target_q_net, tau=1.0)
-        self.env = env
-        self.buffer = BaseBuffer(buffer_size)
-        self.target_update = target_update
-        self.eps = 1.
-        self.eps_end = eps_end
-        self.exploration_fraction = exploration_fraction
-        self.batch_size = batch_size
-        self.gamma = gamma
-        self.train_every_n_steps = train_every_n_steps
-        self.n_grad_steps = n_grad_steps
-        self.lr = lr
-        self.tau = tau
-        self.reg_weight = reg_weight
-        self.n_agents = n_agents
-        self.device = 'cpu'
-        self.optimizer = torch.optim.AdamW(self.q_net.parameters(), lr=self.lr)
-        self.max_grad_norm = max_grad_norm
-        self.running_reward = deque(maxlen=5)
-        self.running_loss = deque(maxlen=5)
-        self._n_updates = 0
-
-    def to(self, device):
-        self.device = device
-        return self
-
-    @staticmethod
-    def weights_init(module, activation='leaky_relu'):
-        if isinstance(module, (nn.Linear, nn.Conv2d)):
-            nn.init.orthogonal_(module.weight, gain=torch.nn.init.calculate_gain(activation))
-            if module.bias is not None:
-                module.bias.data.fill_(0.0)
-
-    def anneal_eps(self, step, n_steps):
-        fraction = min(float(step) / int(self.exploration_fraction*n_steps), 1.0)
-        self.eps = 1 + fraction * (self.eps_end - 1)
-
-    def get_action(self, obs) -> Union[int, np.ndarray]:
-        o = torch.from_numpy(obs).unsqueeze(0) if self.n_agents <= 1 else torch.from_numpy(obs)
-        if np.random.rand() > self.eps:
-            action = self.q_net.act(o.float())
-        else:
-            action = np.array([self.env.action_space.sample() for _ in range(self.n_agents)])
-        return action
-
-    def learn(self, n_steps):
-        step = 0
-        while step < n_steps:
-            obs, done = self.env.reset(), False
-            total_reward = 0
-            while not done:
-
-                action = self.get_action(obs)
-
-                next_obs, reward, done, info = self.env.step(action if not len(action) == 1 else action[0])
-
-                experience = Experience(observation=obs, next_observation=next_obs, action=action, reward=reward, done=done)  # do we really need to copy?
-                self.buffer.add(experience)
-                # end of step routine
-                obs = next_obs
-                step += 1
-                total_reward += reward
-                self.anneal_eps(step, n_steps)
-
-                if step % self.train_every_n_steps == 0:
-                    self.train()
-                    self._n_updates += 1
-                if step % self.target_update == 0:
-                    print('UPDATE')
-                    soft_update(self.q_net, self.target_q_net, tau=self.tau)
-
-            self.running_reward.append(total_reward)
-            if step % 10 == 0:
-                print(f'Step: {step} ({(step/n_steps)*100:.2f}%)\tRunning reward: {sum(list(self.running_reward))/len(self.running_reward):.2f}\t'
-                      f' eps: {self.eps:.4f}\tRunning loss: {sum(list(self.running_loss))/len(self.running_loss):.4f}\tUpdates:{self._n_updates}')
-
-    def _training_routine(self, obs, next_obs, action):
-        current_q_values = self.q_net(obs)
-        current_q_values = torch.gather(current_q_values, dim=-1, index=action)
-        next_q_values_raw = self.target_q_net(next_obs).max(dim=-1)[0].reshape(-1, 1).detach()
-        return current_q_values, next_q_values_raw
-
-    def _backprop_loss(self, loss):
-        # log loss
-        self.running_loss.append(loss.item())
-        # Optimize the model
-        self.optimizer.zero_grad()
-        loss.backward()
-        torch.nn.utils.clip_grad_norm_(self.q_net.parameters(), self.max_grad_norm)
-        self.optimizer.step()
-
-    def train(self):
-        if len(self.buffer) < self.batch_size: return
-        for _ in range(self.n_grad_steps):
-
-            experience = self.buffer.sample(self.batch_size, cer=self.train_every_n_steps)
-            if self.n_agents <= 1:
-                pred_q, target_q_raw = self._training_routine(experience.observation,
-                                                              experience.next_observation,
-                                                              experience.action)
-
-            else:
-                pred_q, target_q_raw = torch.zeros((self.batch_size, 1)), torch.zeros((self.batch_size, 1))
-                for agent_i in range(self.n_agents):
-                    q_values, next_q_values_raw = self._training_routine(experience.observation[:, agent_i],
-                                                                         experience.next_observation[:, agent_i],
-                                                                         experience.action[:, agent_i].unsqueeze(-1))
-                    pred_q += q_values
-                    target_q_raw += next_q_values_raw
-            target_q = experience.reward  + (1 - experience.done) * self.gamma * target_q_raw
-            loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - target_q, 2))
-            self._backprop_loss(loss)
-
-
-class MunchhausenQLearner(BaseQlearner):
-    def __init__(self, *args, temperature=0.03, alpha=0.9, clip_l0=-1.0, **kwargs):
-        super(MunchhausenQLearner, self).__init__(*args, **kwargs)
-        assert self.n_agents == 1, 'M-DQN currently only supports single agent training'
-        self.temperature = temperature
-        self.alpha = alpha
-        self.clip0 = clip_l0
-
-    def tau_ln_pi(self, qs):
-        # computes log(softmax(qs/temperature))
-        # Custom log-sum-exp trick from page 18 to compute the log-policy terms
-        v_k = qs.max(-1)[0].unsqueeze(-1)
-        advantage = qs - v_k
-        logsum = torch.logsumexp(advantage / self.temperature, -1).unsqueeze(-1)
-        tau_ln_pi = advantage - self.temperature * logsum
-        return tau_ln_pi
-
-    def train(self):
-        if len(self.buffer) < self.batch_size: return
-        for _ in range(self.n_grad_steps):
-
-            experience = self.buffer.sample(self.batch_size, cer=self.train_every_n_steps)
-
-            q_target_next = self.target_q_net(experience.next_observation).detach()
-            tau_log_pi_next = self.tau_ln_pi(q_target_next)
-
-            q_k_targets = self.target_q_net(experience.observation).detach()
-            log_pi = self.tau_ln_pi(q_k_targets)
-
-            pi_target = F.softmax(q_target_next / self.temperature, dim=-1)
-            q_target = (self.gamma * (pi_target * (q_target_next - tau_log_pi_next) * (1 - experience.done)).sum(-1)).unsqueeze(-1)
-
-            munchausen_addon = log_pi.gather(-1, experience.action)
-
-            munchausen_reward = (experience.reward + self.alpha * torch.clamp(munchausen_addon, min=self.clip0, max=0))
-
-            # Compute Q targets for current states
-            m_q_target = munchausen_reward + q_target
-
-            # Get expected Q values from local model
-            q_k = self.q_net(experience.observation)
-            pred_q = q_k.gather(-1, experience.action)
-
-            # Compute loss
-            loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - m_q_target, 2))
-            self._backprop_loss(loss)
-
-
-if __name__ == '__main__':
-    from environments.factory.simple_factory import SimpleFactory, DirtProperties, MovementProperties
-
-    N_AGENTS = 1
-
-    dirt_props = DirtProperties(clean_amount=3, gain_amount=0.2, max_global_amount=30,
-                                max_local_amount=5, spawn_frequency=1, max_spawn_ratio=0.05)
-    move_props = MovementProperties(allow_diagonal_movement=True,
-                                    allow_square_movement=True,
-                                    allow_no_op=False)
-    env = SimpleFactory(dirt_properties=dirt_props, movement_properties=move_props, n_agents=N_AGENTS, pomdp_radius=2,  max_steps=400, omit_agent_slice_in_obs=False, combin_agent_slices_in_obs=True)
-
-    dqn, target_dqn = BaseDDQN(), BaseDDQN()
-    learner = MunchhausenQLearner(dqn, target_dqn, env, 40000, target_update=3500, lr=0.0007, gamma=0.99, n_agents=N_AGENTS, tau=0.95, max_grad_norm=10,
-                           train_every_n_steps=4, eps_end=0.025, n_grad_steps=1, reg_weight=0.1, exploration_fraction=0.25, batch_size=64)
-    learner.learn(100000)

algorithms/m_q_learner.py

@@ -0,0 +1,53 @@
+import torch
+import torch.nn.functional as F
+from algorithms.q_learner import QLearner
+
+
+class MQLearner(QLearner):
+    # Munchhausen Q-Learning
+    def __init__(self, *args, temperature=0.03, alpha=0.9, clip_l0=-1.0, **kwargs):
+        super(MQLearner, self).__init__(*args, **kwargs)
+        assert self.n_agents == 1, 'M-DQN currently only supports single agent training'
+        self.temperature = temperature
+        self.alpha = alpha
+        self.clip0 = clip_l0
+
+    def tau_ln_pi(self, qs):
+        # computes log(softmax(qs/temperature))
+        # Custom log-sum-exp trick from page 18 to compute the log-policy terms
+        v_k = qs.max(-1)[0].unsqueeze(-1)
+        advantage = qs - v_k
+        logsum = torch.logsumexp(advantage / self.temperature, -1).unsqueeze(-1)
+        tau_ln_pi = advantage - self.temperature * logsum
+        return tau_ln_pi
+
+    def train(self):
+        if len(self.buffer) < self.batch_size: return
+        for _ in range(self.n_grad_steps):
+
+            experience = self.buffer.sample(self.batch_size, cer=self.train_every_n_steps)
+
+            with torch.no_grad():
+                q_target_next = self.target_q_net(experience.next_observation)
+                tau_log_pi_next = self.tau_ln_pi(q_target_next)
+
+                q_k_targets = self.target_q_net(experience.observation)
+                log_pi = self.tau_ln_pi(q_k_targets)
+
+                pi_target = F.softmax(q_target_next / self.temperature, dim=-1)
+                q_target = (self.gamma * (pi_target * (q_target_next - tau_log_pi_next) * (1 - experience.done)).sum(-1)).unsqueeze(-1)
+
+                munchausen_addon = log_pi.gather(-1, experience.action)
+
+                munchausen_reward = (experience.reward + self.alpha * torch.clamp(munchausen_addon, min=self.clip0, max=0))
+
+                # Compute Q targets for current states
+                m_q_target = munchausen_reward + q_target
+
+            # Get expected Q values from local model
+            q_k = self.q_net(experience.observation)
+            pred_q = q_k.gather(-1, experience.action)
+
+            # Compute loss
+            loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - m_q_target, 2))
+            self._backprop_loss(loss)

algorithms/q_learner.py

@@ -0,0 +1,144 @@
+from typing import Union
+import gym
+import torch
+import torch.nn as nn
+import numpy as np
+from collections import deque
+from pathlib import Path
+import yaml
+from algorithms.common import BaseLearner, BaseBuffer, soft_update, Experience
+
+
+class QLearner(BaseLearner):
+    def __init__(self, q_net, target_q_net, env, buffer_size=1e5, target_update=3000, eps_end=0.05, n_agents=1,
+                 gamma=0.99, train_every_n_steps=4, n_grad_steps=1, tau=1.0, max_grad_norm=10, weight_decay=1e-2,
+                 exploration_fraction=0.2, batch_size=64, lr=1e-4, reg_weight=0.0, eps_start=1):
+        super(QLearner, self).__init__(env, n_agents, lr)
+        self.q_net = q_net
+        self.target_q_net = target_q_net
+        self.target_q_net.eval()
+        soft_update(self.q_net, self.target_q_net, tau=1.0)
+        self.buffer = BaseBuffer(buffer_size)
+        self.target_update = target_update
+        self.eps = eps_start
+        self.eps_start = eps_start
+        self.eps_end = eps_end
+        self.exploration_fraction = exploration_fraction
+        self.batch_size = batch_size
+        self.gamma = gamma
+        self.train_every_n_steps = train_every_n_steps
+        self.n_grad_steps = n_grad_steps
+        self.tau = tau
+        self.reg_weight = reg_weight
+        self.weight_decay = weight_decay
+        self.optimizer = torch.optim.AdamW(self.q_net.parameters(),
+                                           lr=self.lr,
+                                           weight_decay=self.weight_decay)
+        self.max_grad_norm = max_grad_norm
+        self.running_reward = deque(maxlen=5)
+        self.running_loss = deque(maxlen=5)
+        self.n_updates = 0
+
+    def save(self, path):
+        path = Path(path)  # no-op if already instance of Path
+        path.mkdir(parents=True, exist_ok=True)
+        hparams = {k: v for k, v in self.__dict__.items() if not(isinstance(v, BaseBuffer) or
+                                                                 isinstance(v, torch.optim.Optimizer) or
+                                                                 isinstance(v, gym.Env) or
+                                                                 isinstance(v, nn.Module))
+                   }
+        hparams.update({'class': self.__class__.__name__})
+        with (path / 'hparams.yaml').open('w') as outfile:
+            yaml.dump(hparams, outfile)
+        torch.save(self.q_net, path / 'q_net.pt')
+
+    def anneal_eps(self, step, n_steps):
+        fraction = min(float(step) / int(self.exploration_fraction*n_steps), 1.0)
+        self.eps = 1 + fraction * (self.eps_end - 1)
+
+    def get_action(self, obs) -> Union[int, np.ndarray]:
+        o = torch.from_numpy(obs).unsqueeze(0) if self.n_agents <= 1 else torch.from_numpy(obs)
+        if np.random.rand() > self.eps:
+            action = self.q_net.act(o.float())
+        else:
+            action = np.array([self.env.action_space.sample() for _ in range(self.n_agents)])
+        return action
+
+    def learn(self, n_steps):
+        step = 0
+        while step < n_steps:
+            obs, done = self.env.reset(), False
+            total_reward = 0
+            while not done:
+
+                action = self.get_action(obs)
+
+                next_obs, reward, done, info = self.env.step(action if not len(action) == 1 else action[0])
+
+                experience = Experience(observation=obs, next_observation=next_obs, action=action, reward=reward, done=done)  # do we really need to copy?
+                self.buffer.add(experience)
+                # end of step routine
+                obs = next_obs
+                step += 1
+                total_reward += reward
+                self.anneal_eps(step, n_steps)
+
+                if step % self.train_every_n_steps == 0:
+                    self.train()
+                    self.n_updates += 1
+                if step % self.target_update == 0:
+                    print('UPDATE')
+                    soft_update(self.q_net, self.target_q_net, tau=self.tau)
+
+            self.running_reward.append(total_reward)
+            if step % 10 == 0:
+                print(f'Step: {step} ({(step/n_steps)*100:.2f}%)\tRunning reward: {sum(list(self.running_reward))/len(self.running_reward):.2f}\t'
+                      f' eps: {self.eps:.4f}\tRunning loss: {sum(list(self.running_loss))/len(self.running_loss):.4f}\tUpdates:{self.n_updates}')
+
+    def _training_routine(self, obs, next_obs, action):
+        current_q_values = self.q_net(obs)
+        current_q_values = torch.gather(current_q_values, dim=-1, index=action)
+        next_q_values_raw = self.target_q_net(next_obs).max(dim=-1)[0].reshape(-1, 1).detach()
+        return current_q_values, next_q_values_raw
+
+    def _backprop_loss(self, loss):
+        # log loss
+        self.running_loss.append(loss.item())
+        # Optimize the model
+        self.optimizer.zero_grad()
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(self.q_net.parameters(), self.max_grad_norm)
+        self.optimizer.step()
+
+    def train(self):
+        if len(self.buffer) < self.batch_size: return
+        for _ in range(self.n_grad_steps):
+            experience = self.buffer.sample(self.batch_size, cer=self.train_every_n_steps)
+            pred_q, target_q_raw = self._training_routine(experience.observation,
+                                                          experience.next_observation,
+                                                          experience.action)
+            target_q = experience.reward + (1 - experience.done) * self.gamma * target_q_raw
+            loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - target_q, 2))
+            self._backprop_loss(loss)
+
+
+
+if __name__ == '__main__':
+    from environments.factory.simple_factory import SimpleFactory, DirtProperties, MovementProperties
+    from algorithms.common import BaseDDQN
+    from algorithms.vdn_learner import VDNLearner
+
+    N_AGENTS = 1
+
+    dirt_props = DirtProperties(clean_amount=3, gain_amount=0.2, max_global_amount=30,
+                                max_local_amount=5, spawn_frequency=1, max_spawn_ratio=0.05)
+    move_props = MovementProperties(allow_diagonal_movement=True,
+                                    allow_square_movement=True,
+                                    allow_no_op=False)
+    env = SimpleFactory(dirt_properties=dirt_props, movement_properties=move_props, n_agents=N_AGENTS, pomdp_radius=2,  max_steps=400, omit_agent_slice_in_obs=False, combin_agent_slices_in_obs=True)
+
+    dqn, target_dqn = BaseDDQN(), BaseDDQN()
+    learner = QLearner(dqn, target_dqn, env, 40000, target_update=3500, lr=0.0007, gamma=0.99, n_agents=N_AGENTS, tau=0.95, max_grad_norm=10,
+                       train_every_n_steps=4, eps_end=0.025, n_grad_steps=1, reg_weight=0.1, exploration_fraction=0.25, batch_size=64)
+    #learner.save(Path(__file__).parent / 'test' / 'testexperiment1337')
+    learner.learn(100000)

algorithms/vdn_learner.py

@@ -0,0 +1,23 @@
+import torch
+from algorithms.q_learner import QLearner
+
+
+class VDNLearner(QLearner):
+    def __init__(self, *args, **kwargs):
+        super(VDNLearner, self).__init__(*args, **kwargs)
+        assert self.n_agents >= 2, 'VDN requires more than one agent, use QLearner instead'
+
+    def train(self):
+        if len(self.buffer) < self.batch_size: return
+        for _ in range(self.n_grad_steps):
+            experience = self.buffer.sample(self.batch_size, cer=self.train_every_n_steps)
+            pred_q, target_q_raw = torch.zeros((self.batch_size, 1)), torch.zeros((self.batch_size, 1))
+            for agent_i in range(self.n_agents):
+                q_values, next_q_values_raw = self._training_routine(experience.observation[:, agent_i],
+                                                                     experience.next_observation[:, agent_i],
+                                                                     experience.action[:, agent_i].unsqueeze(-1))
+                pred_q += q_values
+                target_q_raw += next_q_values_raw
+            target_q = experience.reward + (1 - experience.done) * self.gamma * target_q_raw
+            loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - target_q, 2))
+            self._backprop_loss(loss)