Setting the same value for num_iterations in .gin config files. This …

…for Atari 100k. Adding support for flatboard on Atari_100k agents and BBF repository. Fixing the requirements.txt file for BBF repository.

PiperOrigin-RevId: 560035815

dopamine/labs/atari_100k/atari_100k_runner.py

@@ -24,6 +24,9 @@
 from dopamine.discrete_domains import iteration_statistics
 from dopamine.discrete_domains import run_experiment
 from dopamine.labs.atari_100k import normalization_utils
+from dopamine.metrics import collector_dispatcher
+from dopamine.metrics import statistics_instance
+
 import gin
 import jax
 import numpy as np
@@ -101,6 +104,13 @@ def __init__(
     self._agent.cache_train_state()
 
     self.log_normalized_scores = log_normalized_scores
+    # Create a collector dispatcher for metrics reporting.
+    self._collector_dispatcher = collector_dispatcher.CollectorDispatcher(
+        self._base_dir)
+    set_collector_dispatcher_fn = getattr(
+        self._agent, 'set_collector_dispatcher', None)
+    if callable(set_collector_dispatcher_fn):
+      set_collector_dispatcher_fn(self._collector_dispatcher)
 
   def _run_one_phase(self,
                      envs,
@@ -455,6 +465,23 @@ def _run_one_iteration(self, iteration):
     num_episodes_eval, average_reward_eval, norm_score_eval = (
         self._run_eval_phase(statistics)
     )
+    self._collector_dispatcher.write([
+        statistics_instance.StatisticsInstance(
+            'Train/NumEpisodes', num_episodes_train, iteration
+        ),
+        statistics_instance.StatisticsInstance(
+            'Train/AverageReturns', average_reward_train, iteration
+        ),
+        statistics_instance.StatisticsInstance(
+            'Train/AverageStepsPerSecond', average_steps_per_second, iteration
+        ),
+        statistics_instance.StatisticsInstance(
+            'Eval/NumEpisodes', num_episodes_eval, iteration
+        ),
+        statistics_instance.StatisticsInstance(
+            'Eval/AverageReturns', average_reward_eval, iteration
+        ),
+    ])
     self._save_tensorboard_summaries(
         iteration,
         num_episodes_train,

dopamine/labs/atari_100k/configs/DrQ.gin

@@ -36,8 +36,8 @@ atari_lib.create_atari_environment.game_name = 'Pong'
 # Atari 100K benchmark doesn't use sticky actions.
 atari_lib.create_atari_environment.sticky_actions = False
 AtariPreprocessing.terminal_on_life_loss = True
-Runner.num_iterations = 1
-Runner.training_steps = 100000  # agent steps
+Runner.num_iterations = 10
+Runner.training_steps = 10000  # agent steps
 MaxEpisodeEvalRunner.num_eval_episodes = 100  # agent episodes
 Runner.max_steps_per_episode = 27000  # agent steps
 

dopamine/labs/atari_100k/configs/DrQ_eps.gin

@@ -37,8 +37,8 @@ atari_lib.create_atari_environment.game_name = 'Pong'
 # Atari 100K benchmark doesn't use sticky actions.
 atari_lib.create_atari_environment.sticky_actions = False
 AtariPreprocessing.terminal_on_life_loss = True
-Runner.num_iterations = 1
-Runner.training_steps = 100000  # agent steps
+Runner.num_iterations = 10
+Runner.training_steps = 10000  # agent steps
 MaxEpisodeEvalRunner.num_eval_episodes = 100  # agent episodes
 Runner.max_steps_per_episode = 27000  # agent steps
 

dopamine/labs/atari_100k/configs/OTRainbow.gin

@@ -37,8 +37,8 @@ atari_lib.create_atari_environment.game_name = 'Pong'
 # Atari 100K benchmark doesn't use sticky actions.
 atari_lib.create_atari_environment.sticky_actions = False
 AtariPreprocessing.terminal_on_life_loss = True
-Runner.num_iterations = 1
-Runner.training_steps = 100000  # agent steps
+Runner.num_iterations = 10
+Runner.training_steps = 10000  # agent steps
 MaxEpisodeEvalRunner.num_eval_episodes = 100  # agent episodes
 Runner.max_steps_per_episode = 27000  # agent steps