erased unnecessary reward input

dreamer.py

@@ -66,7 +66,7 @@ class Dreamer(nn.Module):
             plan2explore=lambda: expl.Plan2Explore(config, self._wm, reward),
         )[config.expl_behavior]().to(self._config.device)
 
-    def __call__(self, obs, reset, state=None, reward=None, training=True):
+    def __call__(self, obs, reset, state=None, training=True):
         step = self._step
         if self._should_reset(step):
             state = None
@@ -295,7 +295,7 @@ def main(config):
                 1,
             )
 
-        def random_agent(o, d, s, r):
+        def random_agent(o, d, s):
             action = random_actor.sample()
             logprob = random_actor.log_prob(action)
             return {"action": action, "logprob": logprob}, None

tools.py

@@ -148,10 +148,9 @@ 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
-            reward = [reward[i] * (1 - done[i]) for i in range(len(envs))]
         # 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, reward)
+        action, agent_state = agent(obs, done, agent_state)
         if isinstance(action, dict):
             action = [
                 {k: np.array(action[k][i].detach().cpu()) for k in action}