erased unused lines of code

dreamer.py

@@ -150,7 +150,6 @@ class Dreamer(nn.Module):
             return tools.OneHotDist(probs=probs).sample()
         else:
             return torch.clip(torchd.normal.Normal(action, amount).sample(), -1, 1)
-        raise NotImplementedError(self._config.action_noise)
 
     def _train(self, data):
         metrics = {}
@@ -181,7 +180,7 @@ def make_dataset(episodes, config):
     return dataset
 
 
-def make_env(config, logger, mode, train_eps, eval_eps):
+def make_env(config, mode):
     suite, task = config.task.split("_", 1)
     if suite == "dmc":
         import envs.dmc as dmc
@@ -260,7 +259,7 @@ def main(config):
     else:
         directory = config.evaldir
     eval_eps = tools.load_episodes(directory, limit=1)
-    make = lambda mode: make_env(config, logger, mode, train_eps, eval_eps)
+    make = lambda mode: make_env(config, mode)
     train_envs = [make("train") for _ in range(config.envs)]
     eval_envs = [make("eval") for _ in range(config.envs)]
     acts = train_envs[0].action_space

tools.py

@@ -123,7 +123,7 @@ class Logger:
 
 
 def simulate(agent, envs, cache, directory, logger, is_eval=False, limit=None, steps=0, episodes=0, state=None):
-    # Initialize or unpack simulation state.
+    # initialize or unpack simulation state
     if state is None:
         step, episode = 0, 0
         done = np.ones(len(envs), bool)
@@ -134,7 +134,7 @@ def simulate(agent, envs, cache, directory, logger, is_eval=False, limit=None, s
     else:
         step, episode, done, length, obs, agent_state, reward = state
     while (steps and step < steps) or (episodes and episode < episodes):
-        # Reset envs if necessary.
+        # reset envs if necessary
         if done.any():
             indices = [index for index, d in enumerate(done) if d]
             results = [envs[i].reset() for i in indices]
@@ -148,7 +148,7 @@ def simulate(agent, envs, cache, directory, logger, is_eval=False, limit=None, s
                 add_to_cache(cache, envs[i].id, t)
             for index, result in zip(indices, results):
                 obs[index] = result
-        # Step agents.
+        # step agents
         obs = {k: np.stack([o[k] for o in obs]) for k in obs[0]}
         action, agent_state = agent(obs, done, agent_state)
         if isinstance(action, dict):
@@ -159,7 +159,7 @@ def simulate(agent, envs, cache, directory, logger, is_eval=False, limit=None, s
         else:
             action = np.array(action)
         assert len(action) == len(envs)
-        # Step envs.
+        # step envs
         results = [e.step(a) for e, a in zip(envs, action)]
         obs, reward, done = zip(*[p[:3] for p in results])
         obs = list(obs)
@@ -169,7 +169,7 @@ def simulate(agent, envs, cache, directory, logger, is_eval=False, limit=None, s
         length += 1
         step += len(envs)
         length *= 1 - done
-        # Add to cache
+        # add to cache
         for a, result, env in zip(action, results, envs):
             o, r, d, info = result
             o = {k: convert(v) for k, v in o.items()}
@@ -267,7 +267,7 @@ class CollectDataset:
     def reset(self):
         obs = self._env.reset()
         transition = obs.copy()
-        # Missing keys will be filled with a zeroed out version of the first
+        # missing keys will be filled with a zeroed out version of the first
         # transition, because we do not know what action information the agent will
         # pass yet.
         transition["reward"] = 0.0