Merge pull request #116 from muupan/update-black

Use latest black

.pfnci/lint.sh

@@ -2,8 +2,7 @@
 
 set -eux
 
-# Use latest black to apply https://github.com/psf/black/issues/1288
-pip3 install git+git://github.com/psf/black.git@88d12f88a97e5e4c8fd0d245df0a311e932fd1e1 flake8 mypy isort
+pip3 install black flake8 mypy isort
 
 black --diff --check pfrl tests examples
 isort --diff --check pfrl tests examples

examples/atari/reproduction/a3c/train_a3c.py

@@ -120,7 +120,10 @@ def make_env(process_idx, test):
         nn.Linear(2592, 256),
         nn.ReLU(),
         pfrl.nn.Branched(
-            nn.Sequential(nn.Linear(256, n_actions), SoftmaxCategoricalHead(),),
+            nn.Sequential(
+                nn.Linear(256, n_actions),
+                SoftmaxCategoricalHead(),
+            ),
             nn.Linear(256, 1),
         ),
     )

examples/atari/reproduction/iqn/train_iqn.py

@@ -124,8 +124,15 @@ def make_env(test):
             nn.ReLU(),
             nn.Flatten(),
         ),
-        phi=nn.Sequential(pfrl.agents.iqn.CosineBasisLinear(64, 3136), nn.ReLU(),),
-        f=nn.Sequential(nn.Linear(3136, 512), nn.ReLU(), nn.Linear(512, n_actions),),
+        phi=nn.Sequential(
+            pfrl.agents.iqn.CosineBasisLinear(64, 3136),
+            nn.ReLU(),
+        ),
+        f=nn.Sequential(
+            nn.Linear(3136, 512),
+            nn.ReLU(),
+            nn.Linear(512, n_actions),
+        ),
     )
 
     # Use the same hyper parameters as https://arxiv.org/abs/1710.10044
@@ -175,7 +182,10 @@ def phi(x):
 
     if args.demo:
         eval_stats = experiments.eval_performance(
-            env=eval_env, agent=agent, n_steps=args.eval_n_steps, n_episodes=None,
+            env=eval_env,
+            agent=agent,
+            n_steps=args.eval_n_steps,
+            n_episodes=None,
         )
         print(
             "n_steps: {} mean: {} median: {} stdev {}".format(

examples/atari/reproduction/rainbow/train_rainbow.py

@@ -110,7 +110,12 @@ def make_env(test):
     n_atoms = 51
     v_max = 10
     v_min = -10
-    q_func = DistributionalDuelingDQN(n_actions, n_atoms, v_min, v_max,)
+    q_func = DistributionalDuelingDQN(
+        n_actions,
+        n_atoms,
+        v_min,
+        v_max,
+    )
 
     # Noisy nets
     pnn.to_factorized_noisy(q_func, sigma_scale=args.noisy_net_sigma)

examples/atari/train_a2c_ale.py

@@ -136,12 +136,18 @@ def make_batch_env(test):
         nn.Linear(2592, 256),
         nn.ReLU(),
         pfrl.nn.Branched(
-            nn.Sequential(nn.Linear(256, n_actions), SoftmaxCategoricalHead(),),
+            nn.Sequential(
+                nn.Linear(256, n_actions),
+                SoftmaxCategoricalHead(),
+            ),
             nn.Linear(256, 1),
         ),
     )
     optimizer = pfrl.optimizers.RMSpropEpsInsideSqrt(
-        model.parameters(), lr=args.lr, eps=args.rmsprop_epsilon, alpha=args.alpha,
+        model.parameters(),
+        lr=args.lr,
+        eps=args.rmsprop_epsilon,
+        alpha=args.alpha,
     )
 
     agent = a2c.A2C(

examples/atari/train_acer_ale.py

@@ -105,8 +105,14 @@ def main():
     )
 
     head = acer.ACERDiscreteActionHead(
-        pi=nn.Sequential(nn.Linear(256, n_actions), SoftmaxCategoricalHead(),),
-        q=nn.Sequential(nn.Linear(256, n_actions), DiscreteActionValueHead(),),
+        pi=nn.Sequential(
+            nn.Linear(256, n_actions),
+            SoftmaxCategoricalHead(),
+        ),
+        q=nn.Sequential(
+            nn.Linear(256, n_actions),
+            DiscreteActionValueHead(),
+        ),
     )
 
     if args.use_lstm:

examples/atari/train_drqn_ale.py

@@ -275,7 +275,10 @@ def phi(x):
 
     if args.demo:
         eval_stats = experiments.eval_performance(
-            env=eval_env, agent=agent, n_steps=None, n_episodes=args.demo_n_episodes,
+            env=eval_env,
+            agent=agent,
+            n_steps=None,
+            n_episodes=args.demo_n_episodes,
         )
         print(
             "n_runs: {} mean: {} median: {} stdev {}".format(

examples/grasping/train_dqn_batch_grasping.py

@@ -53,7 +53,10 @@ def __init__(self, env, max_steps):
         self._max_steps = max_steps
         self._elapsed_steps = 0
         self.observation_space = gym.spaces.Tuple(
-            (env.observation_space, gym.spaces.Discrete(self._max_steps + 1),)
+            (
+                env.observation_space,
+                gym.spaces.Discrete(self._max_steps + 1),
+            )
         )
 
     def reset(self):

examples/optuna/optuna_dqn_obs1d.py

@@ -222,7 +222,9 @@ def suggest(trial, steps):
         max(1e3, hyperparams["decay_steps"] // 2), rbuf_capacity
     )
     hyperparams["replay_start_size"] = trial.suggest_int(
-        "replay_start_size", min_replay_start_size, max_replay_start_size,
+        "replay_start_size",
+        min_replay_start_size,
+        max_replay_start_size,
     )
     # target_update_interval should be a multiple of update_interval
     hyperparams["update_interval"] = trial.suggest_int("update_interval", 1, 8)
@@ -239,7 +241,10 @@ def main():
 
     # training parameters
     parser.add_argument(
-        "--env", type=str, default="LunarLander-v2", help="OpenAI Gym Environment ID.",
+        "--env",
+        type=str,
+        default="LunarLander-v2",
+        help="OpenAI Gym Environment ID.",
     )
     parser.add_argument(
         "--outdir",
@@ -251,7 +256,10 @@ def main():
         ),
     )
     parser.add_argument(
-        "--seed", type=int, default=0, help="Random seed for randomizer.",
+        "--seed",
+        type=int,
+        default=0,
+        help="Random seed for randomizer.",
     )
     parser.add_argument(
         "--monitor",
@@ -289,7 +297,10 @@ def main():
         help="Frequency (in timesteps) of evaluation phase.",
     )
     parser.add_argument(
-        "--batch-size", type=int, default=64, help="Training batch size.",
+        "--batch-size",
+        type=int,
+        default=64,
+        help="Training batch size.",
     )
 
     # Optuna related args
@@ -361,7 +372,9 @@ def main():
             help="Setting percentile == 50.0 is equivalent to the MedianPruner.",
         )
         parser.add_argument(
-            "--n-startup-trials", type=int, default=5,
+            "--n-startup-trials",
+            type=int,
+            default=5,
         )
         parser.add_argument(
             "--n-warmup-steps",
@@ -417,7 +430,8 @@ def objective(trial):
         pruner = optuna.pruners.NopPruner()
     elif args.optuna_pruner == "ThresholdPruner":
         pruner = optuna.pruners.ThresholdPruner(
-            lower=args.lower, n_warmup_steps=args.n_warmup_steps,
+            lower=args.lower,
+            n_warmup_steps=args.n_warmup_steps,
         )
     elif args.optuna_pruner == "PercentilePruner":
         pruner = optuna.pruners.PercentilePruner(

examples/slimevolley/train_rainbow.py

@@ -210,7 +210,10 @@ def phi(x):
 
     if args.demo:
         eval_stats = experiments.eval_performance(
-            env=eval_env, agent=agent, n_steps=None, n_episodes=args.eval_n_episodes,
+            env=eval_env,
+            agent=agent,
+            n_steps=None,
+            n_episodes=args.eval_n_episodes,
         )
         print(
             "n_episodes: {} mean: {} median: {} stdev {}".format(

pfrl/action_value.py

@@ -174,7 +174,9 @@ def params(self):
 
     def __getitem__(self, i):
         return DistributionalDiscreteActionValue(
-            self.q_dist[i], self.z_values, q_values_formatter=self.q_values_formatter,
+            self.q_dist[i],
+            self.z_values,
+            q_values_formatter=self.q_values_formatter,
         )
 
 
@@ -209,9 +211,11 @@ def evaluate_actions_as_quantiles(self, actions):
         ]
 
     def __repr__(self):
-        return "QuantileDiscreteActionValue greedy_actions:{} q_values:{}".format(  # NOQA
-            self.greedy_actions.detach().cpu().numpy(),
-            self.q_values_formatter(self.q_values.detach().cpu().numpy()),
+        return (
+            "QuantileDiscreteActionValue greedy_actions:{} q_values:{}".format(  # NOQA
+                self.greedy_actions.detach().cpu().numpy(),
+                self.q_values_formatter(self.q_values.detach().cpu().numpy()),
+            )
         )
 
     @property
@@ -220,7 +224,8 @@ def params(self):
 
     def __getitem__(self, i):
         return QuantileDiscreteActionValue(
-            quantiles=self.quantiles[i], q_values_formatter=self.q_values_formatter,
+            quantiles=self.quantiles[i],
+            q_values_formatter=self.q_values_formatter,
         )
 
 
@@ -276,7 +281,9 @@ def greedy_actions(self):
     @lazy_property
     def max(self):
         if self.min_action is None and self.max_action is None:
-            return self.v.reshape(self.batch_size,)
+            return self.v.reshape(
+                self.batch_size,
+            )
         else:
             return self.evaluate_actions(self.greedy_actions)
 
@@ -288,7 +295,9 @@ def evaluate_actions(self, actions):
                 torch.matmul(u_minus_mu[:, None, :], self.mat), u_minus_mu[:, :, None]
             )[:, 0, 0]
         )
-        return a + self.v.reshape(self.batch_size,)
+        return a + self.v.reshape(
+            self.batch_size,
+        )
 
     def compute_advantage(self, actions):
         return self.evaluate_actions(actions) - self.max

pfrl/agents/a3c.py

@@ -168,7 +168,9 @@ def update(self, statevar):
         )
         if self.recurrent:
             (batch_distrib, batch_v), _ = pack_and_forward(
-                self.model, [batch_obs], self.past_recurrent_state[self.t_start],
+                self.model,
+                [batch_obs],
+                self.past_recurrent_state[self.t_start],
             )
         else:
             batch_distrib, batch_v = self.model(batch_obs)

pfrl/agents/acer.py

@@ -188,13 +188,17 @@ def deepcopy_distribution(distrib):
     """
     if isinstance(distrib, torch.distributions.Independent):
         return torch.distributions.Independent(
-            deepcopy_distribution(distrib.base_dist), distrib.reinterpreted_batch_ndims,
+            deepcopy_distribution(distrib.base_dist),
+            distrib.reinterpreted_batch_ndims,
         )
     elif isinstance(distrib, torch.distributions.Categorical):
-        return torch.distributions.Categorical(logits=distrib.logits.clone().detach(),)
+        return torch.distributions.Categorical(
+            logits=distrib.logits.clone().detach(),
+        )
     elif isinstance(distrib, torch.distributions.Normal):
         return torch.distributions.Normal(
-            loc=distrib.loc.clone().detach(), scale=distrib.scale.clone().detach(),
+            loc=distrib.loc.clone().detach(),
+            scale=distrib.scale.clone().detach(),
         )
     else:
         raise NotImplementedError("{} is not supported by ACER".format(type(distrib)))
@@ -624,7 +628,9 @@ def update_from_replay(self):
                         (avg_action_distrib, _, _),
                         shared_recurrent_state,
                     ) = one_step_forward(
-                        self.shared_average_model, bs, shared_recurrent_state,
+                        self.shared_average_model,
+                        bs,
+                        shared_recurrent_state,
                     )
                 else:
                     avg_action_distrib, _, _ = self.shared_average_model(bs)
@@ -731,7 +737,9 @@ def _act_train(self, obs):
                     (avg_action_distrib, _, _),
                     self.shared_recurrent_states,
                 ) = one_step_forward(
-                    self.shared_average_model, statevar, self.shared_recurrent_states,
+                    self.shared_average_model,
+                    statevar,
+                    self.shared_recurrent_states,
                 )
             else:
                 avg_action_distrib, _, _ = self.shared_average_model(statevar)
@@ -789,7 +797,10 @@ def _observe_train(self, state, reward, done, reset):
         self.past_rewards[self.t - 1] = reward
         if self.process_idx == 0:
             self.logger.debug(
-                "t:%s r:%s a:%s", self.t, reward, self.last_action,
+                "t:%s r:%s a:%s",
+                self.t,
+                reward,
+                self.last_action,
             )
 
         if self.t - self.t_start == self.t_max or done or reset:

pfrl/agents/al.py

@@ -27,7 +27,9 @@ def _compute_y_and_t(self, exp_batch):
 
         if self.recurrent:
             qout, _ = pack_and_forward(
-                self.model, batch_state, exp_batch["recurrent_state"],
+                self.model,
+                batch_state,
+                exp_batch["recurrent_state"],
             )
         else:
             qout = self.model(batch_state)
@@ -42,7 +44,9 @@ def _compute_y_and_t(self, exp_batch):
         with torch.no_grad():
             if self.recurrent:
                 target_qout, _ = pack_and_forward(
-                    self.target_model, batch_state, exp_batch["recurrent_state"],
+                    self.target_model,
+                    batch_state,
+                    exp_batch["recurrent_state"],
                 )
                 target_next_qout, _ = pack_and_forward(
                     self.target_model,
@@ -53,7 +57,9 @@ def _compute_y_and_t(self, exp_batch):
                 target_qout = self.target_model(batch_state)
                 target_next_qout = self.target_model(batch_next_state)
 
-            next_q_max = target_next_qout.max.reshape(batch_size,)
+            next_q_max = target_next_qout.max.reshape(
+                batch_size,
+            )
 
             batch_rewards = exp_batch["reward"]
             batch_terminal = exp_batch["is_state_terminal"]

pfrl/agents/categorical_double_dqn.py

@@ -24,7 +24,9 @@ def _compute_target_values(self, exp_batch):
                     exp_batch["next_recurrent_state"],
                 )
                 next_qout, _ = pack_and_forward(
-                    self.model, batch_next_state, exp_batch["next_recurrent_state"],
+                    self.model,
+                    batch_next_state,
+                    exp_batch["next_recurrent_state"],
                 )
             else:
                 target_next_qout = self.target_model(batch_next_state)

pfrl/agents/categorical_dqn.py

@@ -117,7 +117,9 @@ def _compute_target_values(self, exp_batch):
         batch_next_state = exp_batch["next_state"]
         if self.recurrent:
             target_next_qout, _ = pack_and_forward(
-                self.target_model, batch_next_state, exp_batch["next_recurrent_state"],
+                self.target_model,
+                batch_next_state,
+                exp_batch["next_recurrent_state"],
             )
         else:
             target_next_qout = self.target_model(batch_next_state)