Make Monitor optional

examples/atari/reproduction/a3c/train_a3c.py

@@ -33,7 +33,7 @@ def main():
     parser.add_argument("--t-max", type=int, default=5)
     parser.add_argument("--beta", type=float, default=1e-2)
     parser.add_argument("--profile", action="store_true")
-    parser.add_argument("--steps", type=int, default=8 * 10 ** 7)
+    parser.add_argument("--steps", type=int, default=8 * 10**7)
     parser.add_argument(
         "--max-frames",
         type=int,
@@ -84,15 +84,15 @@ def main():
     # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
     # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
     process_seeds = np.arange(args.processes) + args.seed * args.processes
-    assert process_seeds.max() < 2 ** 31
+    assert process_seeds.max() < 2**31
 
     args.outdir = experiments.prepare_output_dir(args, args.outdir)
     print("Output files are saved in {}".format(args.outdir))
 
     def make_env(process_idx, test):
         # Use different random seeds for train and test envs
         process_seed = process_seeds[process_idx]
-        env_seed = 2 ** 31 - 1 - process_seed if test else process_seed
+        env_seed = 2**31 - 1 - process_seed if test else process_seed
         env = atari_wrappers.wrap_deepmind(
             atari_wrappers.make_atari(args.env, max_frames=args.max_frames),
             episode_life=not test,

examples/atari/reproduction/dqn/train_dqn.py

@@ -64,13 +64,13 @@ def main():
     parser.add_argument(
         "--steps",
         type=int,
-        default=5 * 10 ** 7,
+        default=5 * 10**7,
         help="Total number of timesteps to train the agent.",
     )
     parser.add_argument(
         "--replay-start-size",
         type=int,
-        default=5 * 10 ** 4,
+        default=5 * 10**4,
         help="Minimum replay buffer size before " + "performing gradient updates.",
     )
     parser.add_argument("--eval-n-steps", type=int, default=125000)
@@ -87,7 +87,7 @@ def main():
 
     # Set different random seeds for train and test envs.
     train_seed = args.seed
-    test_seed = 2 ** 31 - 1 - args.seed
+    test_seed = 2**31 - 1 - args.seed
 
     args.outdir = experiments.prepare_output_dir(args, args.outdir)
     print("Output files are saved in {}".format(args.outdir))
@@ -133,12 +133,12 @@ def make_env(test):
         centered=True,
     )
 
-    rbuf = replay_buffers.ReplayBuffer(10 ** 6)
+    rbuf = replay_buffers.ReplayBuffer(10**6)
 
     explorer = explorers.LinearDecayEpsilonGreedy(
         start_epsilon=1.0,
         end_epsilon=0.1,
-        decay_steps=10 ** 6,
+        decay_steps=10**6,
         random_action_func=lambda: np.random.randint(n_actions),
     )
 
@@ -155,7 +155,7 @@ def phi(x):
         gamma=0.99,
         explorer=explorer,
         replay_start_size=args.replay_start_size,
-        target_update_interval=10 ** 4,
+        target_update_interval=10**4,
         clip_delta=True,
         update_interval=4,
         batch_accumulator="sum",

examples/atari/reproduction/iqn/train_iqn.py

@@ -31,18 +31,18 @@ def main():
         "--pretrained-type", type=str, default="best", choices=["best", "final"]
     )
     parser.add_argument("--load", type=str, default=None)
-    parser.add_argument("--final-exploration-frames", type=int, default=10 ** 6)
+    parser.add_argument("--final-exploration-frames", type=int, default=10**6)
     parser.add_argument("--final-epsilon", type=float, default=0.01)
     parser.add_argument("--eval-epsilon", type=float, default=0.001)
-    parser.add_argument("--steps", type=int, default=5 * 10 ** 7)
+    parser.add_argument("--steps", type=int, default=5 * 10**7)
     parser.add_argument(
         "--max-frames",
         type=int,
         default=30 * 60 * 60,  # 30 minutes with 60 fps
         help="Maximum number of frames for each episode.",
     )
-    parser.add_argument("--replay-start-size", type=int, default=5 * 10 ** 4)
-    parser.add_argument("--target-update-interval", type=int, default=10 ** 4)
+    parser.add_argument("--replay-start-size", type=int, default=5 * 10**4)
+    parser.add_argument("--target-update-interval", type=int, default=10**4)
     parser.add_argument("--eval-interval", type=int, default=250000)
     parser.add_argument("--eval-n-steps", type=int, default=125000)
     parser.add_argument("--update-interval", type=int, default=4)
@@ -85,7 +85,7 @@ def main():
 
     # Set different random seeds for train and test envs.
     train_seed = args.seed
-    test_seed = 2 ** 31 - 1 - args.seed
+    test_seed = 2**31 - 1 - args.seed
 
     args.outdir = experiments.prepare_output_dir(args, args.outdir)
     print("Output files are saved in {}".format(args.outdir))
@@ -138,7 +138,7 @@ def make_env(test):
     # Use the same hyper parameters as https://arxiv.org/abs/1710.10044
     opt = torch.optim.Adam(q_func.parameters(), lr=5e-5, eps=1e-2 / args.batch_size)
 
-    rbuf = replay_buffers.ReplayBuffer(10 ** 6)
+    rbuf = replay_buffers.ReplayBuffer(10**6)
 
     explorer = explorers.LinearDecayEpsilonGreedy(
         1.0,

examples/atari/reproduction/rainbow/train_rainbow.py

@@ -35,14 +35,14 @@ def main():
     parser.add_argument("--load", type=str, default=None)
     parser.add_argument("--eval-epsilon", type=float, default=0.0)
     parser.add_argument("--noisy-net-sigma", type=float, default=0.5)
-    parser.add_argument("--steps", type=int, default=5 * 10 ** 7)
+    parser.add_argument("--steps", type=int, default=5 * 10**7)
     parser.add_argument(
         "--max-frames",
         type=int,
         default=30 * 60 * 60,  # 30 minutes with 60 fps
         help="Maximum number of frames for each episode.",
     )
-    parser.add_argument("--replay-start-size", type=int, default=2 * 10 ** 4)
+    parser.add_argument("--replay-start-size", type=int, default=2 * 10**4)
     parser.add_argument("--eval-n-steps", type=int, default=125000)
     parser.add_argument("--eval-interval", type=int, default=250000)
     parser.add_argument(
@@ -77,7 +77,7 @@ def main():
 
     # Set different random seeds for train and test envs.
     train_seed = args.seed
-    test_seed = 2 ** 31 - 1 - args.seed
+    test_seed = 2**31 - 1 - args.seed
 
     args.outdir = experiments.prepare_output_dir(args, args.outdir)
     print("Output files are saved in {}".format(args.outdir))
@@ -123,14 +123,14 @@ def make_env(test):
     explorer = explorers.Greedy()
 
     # Use the same hyper parameters as https://arxiv.org/abs/1710.02298
-    opt = torch.optim.Adam(q_func.parameters(), 6.25e-5, eps=1.5 * 10 ** -4)
+    opt = torch.optim.Adam(q_func.parameters(), 6.25e-5, eps=1.5 * 10**-4)
 
     # Prioritized Replay
     # Anneal beta from beta0 to 1 throughout training
     update_interval = 4
     betasteps = args.steps / update_interval
     rbuf = replay_buffers.PrioritizedReplayBuffer(
-        10 ** 6,
+        10**6,
         alpha=0.5,
         beta0=0.4,
         betasteps=betasteps,

examples/atari/train_a2c_ale.py

@@ -28,7 +28,7 @@ def main():
         default=30 * 60 * 60,  # 30 minutes with 60 fps
         help="Maximum number of frames for each episode.",
     )
-    parser.add_argument("--steps", type=int, default=8 * 10 ** 7)
+    parser.add_argument("--steps", type=int, default=8 * 10**7)
     parser.add_argument("--update-steps", type=int, default=5)
     parser.add_argument("--lr", type=float, default=7e-4)
     parser.add_argument("--gamma", type=float, default=0.99, help="discount factor")
@@ -43,7 +43,7 @@ def main():
     parser.add_argument(
         "--alpha", type=float, default=0.99, help="RMSprop optimizer alpha"
     )
-    parser.add_argument("--eval-interval", type=int, default=10 ** 6)
+    parser.add_argument("--eval-interval", type=int, default=10**6)
     parser.add_argument("--eval-n-runs", type=int, default=10)
     parser.add_argument("--demo", action="store_true", default=False)
     parser.add_argument("--load", type=str, default="")
@@ -92,15 +92,15 @@ def main():
     # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
     # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
     process_seeds = np.arange(args.num_envs) + args.seed * args.num_envs
-    assert process_seeds.max() < 2 ** 31
+    assert process_seeds.max() < 2**31
 
     args.outdir = experiments.prepare_output_dir(args, args.outdir)
     print("Output files are saved in {}".format(args.outdir))
 
     def make_env(process_idx, test):
         # Use different random seeds for train and test envs
         process_seed = process_seeds[process_idx]
-        env_seed = 2 ** 31 - 1 - process_seed if test else process_seed
+        env_seed = 2**31 - 1 - process_seed if test else process_seed
         env = atari_wrappers.wrap_deepmind(
             atari_wrappers.make_atari(args.env, max_frames=args.max_frames),
             episode_life=not test,

examples/atari/train_acer_ale.py

@@ -38,15 +38,15 @@ def main():
     parser.add_argument("--n-times-replay", type=int, default=4)
     parser.add_argument("--beta", type=float, default=1e-2)
     parser.add_argument("--profile", action="store_true")
-    parser.add_argument("--steps", type=int, default=10 ** 7)
+    parser.add_argument("--steps", type=int, default=10**7)
     parser.add_argument(
         "--max-frames",
         type=int,
         default=30 * 60 * 60,  # 30 minutes with 60 fps
         help="Maximum number of frames for each episode.",
     )
     parser.add_argument("--lr", type=float, default=7e-4)
-    parser.add_argument("--eval-interval", type=int, default=10 ** 5)
+    parser.add_argument("--eval-interval", type=int, default=10**5)
     parser.add_argument("--eval-n-runs", type=int, default=10)
     parser.add_argument("--use-lstm", action="store_true")
     parser.add_argument("--demo", action="store_true", default=False)
@@ -87,7 +87,7 @@ def main():
     # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
     # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
     process_seeds = np.arange(args.processes) + args.seed * args.processes
-    assert process_seeds.max() < 2 ** 31
+    assert process_seeds.max() < 2**31
 
     args.outdir = experiments.prepare_output_dir(args, args.outdir)
     print("Output files are saved in {}".format(args.outdir))
@@ -130,7 +130,7 @@ def main():
         model.parameters(), lr=args.lr, eps=4e-3, alpha=0.99
     )
 
-    replay_buffer = EpisodicReplayBuffer(10 ** 6 // args.processes)
+    replay_buffer = EpisodicReplayBuffer(10**6 // args.processes)
 
     def phi(x):
         # Feature extractor
@@ -156,7 +156,7 @@ def phi(x):
     def make_env(process_idx, test):
         # Use different random seeds for train and test envs
         process_seed = process_seeds[process_idx]
-        env_seed = 2 ** 31 - 1 - process_seed if test else process_seed
+        env_seed = 2**31 - 1 - process_seed if test else process_seed
         env = atari_wrappers.wrap_deepmind(
             atari_wrappers.make_atari(args.env, max_frames=args.max_frames),
             episode_life=not test,

examples/atari/train_categorical_dqn_ale.py

@@ -24,19 +24,19 @@ def main():
     parser.add_argument("--gpu", type=int, default=0)
     parser.add_argument("--demo", action="store_true", default=False)
     parser.add_argument("--load", type=str, default=None)
-    parser.add_argument("--final-exploration-frames", type=int, default=10 ** 6)
+    parser.add_argument("--final-exploration-frames", type=int, default=10**6)
     parser.add_argument("--final-epsilon", type=float, default=0.1)
     parser.add_argument("--eval-epsilon", type=float, default=0.05)
-    parser.add_argument("--steps", type=int, default=10 ** 7)
+    parser.add_argument("--steps", type=int, default=10**7)
     parser.add_argument(
         "--max-frames",
         type=int,
         default=30 * 60 * 60,  # 30 minutes with 60 fps
         help="Maximum number of frames for each episode.",
     )
-    parser.add_argument("--replay-start-size", type=int, default=5 * 10 ** 4)
-    parser.add_argument("--target-update-interval", type=int, default=10 ** 4)
-    parser.add_argument("--eval-interval", type=int, default=10 ** 5)
+    parser.add_argument("--replay-start-size", type=int, default=5 * 10**4)
+    parser.add_argument("--target-update-interval", type=int, default=10**4)
+    parser.add_argument("--eval-interval", type=int, default=10**5)
     parser.add_argument("--update-interval", type=int, default=4)
     parser.add_argument("--eval-n-runs", type=int, default=10)
     parser.add_argument("--batch-size", type=int, default=32)
@@ -71,7 +71,7 @@ def main():
 
     # Set different random seeds for train and test envs.
     train_seed = args.seed
-    test_seed = 2 ** 31 - 1 - args.seed
+    test_seed = 2**31 - 1 - args.seed
 
     args.outdir = experiments.prepare_output_dir(args, args.outdir)
     print("Output files are saved in {}".format(args.outdir))
@@ -120,7 +120,7 @@ def make_env(test):
     # Use the same hyper parameters as https://arxiv.org/abs/1707.06887
     opt = torch.optim.Adam(q_func.parameters(), 2.5e-4, eps=1e-2 / args.batch_size)
 
-    rbuf = replay_buffers.ReplayBuffer(10 ** 6)
+    rbuf = replay_buffers.ReplayBuffer(10**6)
 
     explorer = explorers.LinearDecayEpsilonGreedy(
         1.0,

examples/atari/train_dqn_ale.py

@@ -86,7 +86,7 @@ def main():
     parser.add_argument(
         "--final-exploration-frames",
         type=int,
-        default=10 ** 6,
+        default=10**6,
         help="Timesteps after which we stop " + "annealing exploration rate",
     )
     parser.add_argument(
@@ -112,7 +112,7 @@ def main():
     parser.add_argument(
         "--steps",
         type=int,
-        default=5 * 10 ** 7,
+        default=5 * 10**7,
         help="Total number of timesteps to train the agent.",
     )
     parser.add_argument(
@@ -124,19 +124,19 @@ def main():
     parser.add_argument(
         "--replay-start-size",
         type=int,
-        default=5 * 10 ** 4,
+        default=5 * 10**4,
         help="Minimum replay buffer size before " + "performing gradient updates.",
     )
     parser.add_argument(
         "--target-update-interval",
         type=int,
-        default=3 * 10 ** 4,
+        default=3 * 10**4,
         help="Frequency (in timesteps) at which " + "the target network is updated.",
     )
     parser.add_argument(
         "--eval-interval",
         type=int,
-        default=10 ** 5,
+        default=10**5,
         help="Frequency (in timesteps) of evaluation phase.",
     )
     parser.add_argument(
@@ -196,7 +196,7 @@ def main():
 
     # Set different random seeds for train and test envs.
     train_seed = args.seed
-    test_seed = 2 ** 31 - 1 - args.seed
+    test_seed = 2**31 - 1 - args.seed
 
     args.outdir = experiments.prepare_output_dir(args, args.outdir)
     print("Output files are saved in {}".format(args.outdir))
@@ -254,14 +254,14 @@ def make_env(test):
         # Anneal beta from beta0 to 1 throughout training
         betasteps = args.steps / args.update_interval
         rbuf = replay_buffers.PrioritizedReplayBuffer(
-            10 ** 6,
+            10**6,
             alpha=0.6,
             beta0=0.4,
             betasteps=betasteps,
             num_steps=args.num_step_return,
         )
     else:
-        rbuf = replay_buffers.ReplayBuffer(10 ** 6, args.num_step_return)
+        rbuf = replay_buffers.ReplayBuffer(10**6, args.num_step_return)
 
     def phi(x):
         # Feature extractor