Add TQC support (#46)

* Add TQC from sb3-contrib

* Update plot script

* Tuned hyperparams for Hopper

* Update plot

* Update hyperparams

* Update plot script: allow to merge files

* Make pytype happy

* Update humanoid params

* Revert Humanoids params

* Fix deps

* Fixes

* Add support for HER + TQC

CHANGELOG.md

@@ -5,6 +5,7 @@
 ### New Features
 - Added support for `HER`
 - Added low-pass filter wrappers in `utils/wrappers.py`
+- Added `TQC` support, implementation from sb3-contrib
 
 ### Bug fixes
 - Fixed `TimeFeatureWrapper` inferring max timesteps

Makefile

@@ -6,7 +6,7 @@ pytest:
 
 # Type check
 type:
-	pytype ${LINT_PATHS}
+	pytype -j auto ${LINT_PATHS}
 
 lint:
 	# stop the build if there are Python syntax errors or undefined names

enjoy.py

@@ -65,11 +65,11 @@ def main():  # noqa: C901
 
     if args.exp_id == 0:
         args.exp_id = get_latest_run_id(os.path.join(folder, algo), env_id)
-        print("Loading latest experiment, id={}".format(args.exp_id))
+        print(f"Loading latest experiment, id={args.exp_id}")
 
     # Sanity checks
     if args.exp_id > 0:
-        log_path = os.path.join(folder, algo, "{}_{}".format(env_id, args.exp_id))
+        log_path = os.path.join(folder, algo, f"{env_id}_{args.exp_id}")
     else:
         log_path = os.path.join(folder, algo)
 
@@ -93,7 +93,7 @@ def main():  # noqa: C901
     if not found:
         raise ValueError(f"No model found for {algo} on {env_id}, path: {model_path}")
 
-    if algo in ["dqn", "ddpg", "sac", "td3"]:
+    if algo in ["dqn", "ddpg", "sac", "td3", "tqc"]:
         args.n_envs = 1
 
     set_random_seed(args.seed)
@@ -134,7 +134,7 @@ def main():  # noqa: C901
     )
 
     kwargs = dict(seed=args.seed)
-    if algo in ["dqn", "ddpg", "sac", "her", "td3"]:
+    if algo in ["dqn", "ddpg", "sac", "her", "td3", "tqc"]:
         # Dummy buffer size as we don't need memory to enjoy the trained agent
         kwargs.update(dict(buffer_size=1))
 
@@ -143,7 +143,7 @@ def main():  # noqa: C901
     obs = env.reset()
 
     # Force deterministic for DQN, DDPG, SAC and HER (that is a wrapper around)
-    deterministic = args.deterministic or algo in ["dqn", "ddpg", "sac", "her", "td3"] and not args.stochastic
+    deterministic = args.deterministic or algo in ["dqn", "ddpg", "sac", "her", "td3", "tqc"] and not args.stochastic
 
     state = None
     episode_reward = 0.0

hyperparams/tqc.yml

@@ -0,0 +1,202 @@
+# Tuned
+MountainCarContinuous-v0:
+  n_timesteps: !!float 50000
+  policy: 'MlpPolicy'
+  learning_rate: !!float 3e-4
+  buffer_size: 50000
+  batch_size: 512
+  ent_coef: 0.1
+  train_freq: 32
+  gradient_steps: 32
+  gamma: 0.9999
+  tau: 0.01
+  learning_starts: 0
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3.67, net_arch=[64, 64])"
+
+Pendulum-v0:
+  n_timesteps: 20000
+  policy: 'MlpPolicy'
+  learning_rate: !!float 1e-3
+  use_sde: True
+  n_episodes_rollout: 1
+  gradient_steps: -1
+  train_freq: -1
+  policy_kwargs: "dict(log_std_init=-2, net_arch=[64, 64])"
+
+LunarLanderContinuous-v2:
+  n_timesteps: !!float 5e5
+  policy: 'MlpPolicy'
+  batch_size: 256
+  learning_starts: 1000
+
+BipedalWalker-v3:
+  n_timesteps: !!float 5e5
+  policy: 'MlpPolicy'
+  learning_rate: !!float 7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+
+# Almost tuned
+# History wrapper of size 2 for better performances
+BipedalWalkerHardcore-v3:
+  n_timesteps: !!float 2e6
+  policy: 'MlpPolicy'
+  learning_rate: lin_7.3e-4
+  buffer_size: 1000000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.99
+  tau: 0.01
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300], use_expln=True)"
+
+# === Bullet envs ===
+
+# Tuned
+HalfCheetahBulletEnv-v0:
+  env_wrapper: utils.wrappers.TimeFeatureWrapper
+  n_timesteps: !!float 1e6
+  policy: 'MlpPolicy'
+  learning_rate: !!float 7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+
+# Tuned
+AntBulletEnv-v0:
+  env_wrapper: utils.wrappers.TimeFeatureWrapper
+  n_timesteps: !!float 1e6
+  policy: 'MlpPolicy'
+  learning_rate: !!float 7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+
+# Tuned
+HopperBulletEnv-v0:
+  env_wrapper: utils.wrappers.TimeFeatureWrapper
+  n_timesteps: !!float 1e6
+  policy: 'MlpPolicy'
+  learning_rate: lin_7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  top_quantiles_to_drop_per_net: 5
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+
+# Tuned
+Walker2DBulletEnv-v0:
+  env_wrapper: utils.wrappers.TimeFeatureWrapper
+  n_timesteps: !!float 1e6
+  policy: 'MlpPolicy'
+  learning_rate: lin_7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+
+
+ReacherBulletEnv-v0:
+  env_wrapper: utils.wrappers.TimeFeatureWrapper
+  n_timesteps: !!float 3e5
+  policy: 'MlpPolicy'
+  learning_rate: !!float 7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+
+
+# Almost tuned
+HumanoidBulletEnv-v0:
+  env_wrapper: utils.wrappers.TimeFeatureWrapper
+  n_timesteps: !!float 1e7
+  policy: 'MlpPolicy'
+  learning_rate: lin_7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  top_quantiles_to_drop_per_net: 5
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+
+InvertedDoublePendulumBulletEnv-v0:
+  env_wrapper: utils.wrappers.TimeFeatureWrapper
+  n_timesteps: !!float 5e5
+  policy: 'MlpPolicy'
+  learning_rate: !!float 7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"
+
+InvertedPendulumSwingupBulletEnv-v0:
+  env_wrapper: utils.wrappers.TimeFeatureWrapper
+  n_timesteps: !!float 3e5
+  policy: 'MlpPolicy'
+  learning_rate: !!float 7.3e-4
+  buffer_size: 300000
+  batch_size: 256
+  ent_coef: 'auto'
+  gamma: 0.98
+  tau: 0.02
+  train_freq: 64
+  gradient_steps: 64
+  learning_starts: 10000
+  use_sde: True
+  policy_kwargs: "dict(log_std_init=-3, net_arch=[400, 300])"

requirements.txt

@@ -1,4 +1,4 @@
-stable-baselines3[extra,tests,docs]>=0.10.0a0
+stable-baselines3[extra,tests,docs]>=0.10.0a1
 box2d-py==2.3.5
 pybullet
 gym-minigrid
@@ -7,3 +7,4 @@ optuna
 pytablewriter
 seaborn
 pyyaml>=5.1
+sb3-contrib>=0.10.0a1

scripts/plot_from_file.py

@@ -30,6 +30,9 @@ def restyle_boxplot(artist_dict, color, gray="#222222", linewidth=1, fliersize=5
 parser = argparse.ArgumentParser("Gather results, plot them and create table")
 parser.add_argument("-i", "--input", help="Input filename (numpy archive)", type=str)
 parser.add_argument("-skip", "--skip-envs", help="Environments to skip", nargs="+", default=[], type=str)
+parser.add_argument("--keep-envs", help="Envs to keep", nargs="+", default=[], type=str)
+parser.add_argument("--skip-keys", help="Keys to skip", nargs="+", default=[], type=str)
+parser.add_argument("--keep-keys", help="Keys to keep", nargs="+", default=[], type=str)
 parser.add_argument("--no-million", action="store_true", default=False, help="Do not convert x-axis to million")
 parser.add_argument("--skip-timesteps", action="store_true", default=False, help="Do not display learning curves")
 parser.add_argument("-o", "--output", help="Output filename (image)", type=str)
@@ -40,6 +43,7 @@ def restyle_boxplot(artist_dict, color, gray="#222222", linewidth=1, fliersize=5
 parser.add_argument("-l", "--labels", help="Custom labels", type=str, nargs="+")
 parser.add_argument("-b", "--boxplot", help="Enable boxplot", action="store_true", default=False)
 parser.add_argument("-latex", "--latex", help="Enable latex support", action="store_true", default=False)
+parser.add_argument("--merge", help="Merge with other results files", nargs="+", default=[], type=str)
 
 args = parser.parse_args()
 
@@ -69,8 +73,26 @@ def restyle_boxplot(artist_dict, color, gray="#222222", linewidth=1, fliersize=5
 
 del results["results_table"]
 
-keys = [key for key in results[list(results.keys())[0]].keys()]
+for filename in args.merge:
+    # Merge other files
+    with open(filename, "rb") as file_handler:
+        results_2 = pickle.load(file_handler)
+        del results_2["results_table"]
+        for key in results.keys():
+            if key in results_2:
+                for new_key in results_2[key].keys():
+                    results[key][new_key] = results_2[key][new_key]
+
+
+keys = [key for key in results[list(results.keys())[0]].keys() if key not in args.skip_keys]
+print(f"keys: {keys}")
+if len(args.keep_keys) > 0:
+    keys = [key for key in keys if key in args.keep_keys]
 envs = [env for env in results.keys() if env not in args.skip_envs]
+
+if len(args.keep_envs) > 0:
+    envs = [env for env in envs if env in args.keep_envs]
+
 labels = {key: key for key in keys}
 if args.labels is not None:
     for key, label in zip(keys, args.labels):
@@ -129,9 +151,10 @@ def restyle_boxplot(artist_dict, color, gray="#222222", linewidth=1, fliersize=5
 # plt.title('Influence of the time feature', fontsize=args.fontsize)
 # plt.title('Influence of the network architecture', fontsize=args.fontsize)
 # plt.title('Influence of the exploration variance $log \sigma$', fontsize=args.fontsize)
-plt.title("Influence of the sampling frequency", fontsize=args.fontsize)
+# plt.title("Influence of the sampling frequency", fontsize=args.fontsize)
 # plt.title('Parallel vs No Parallel Sampling', fontsize=args.fontsize)
 # plt.title('Influence of the exploration function input', fontsize=args.fontsize)
+plt.title("PyBullet envs", fontsize=args.fontsize)
 plt.xticks(fontsize=13)
 plt.xlabel("Environment", fontsize=args.fontsize)
 plt.ylabel("Score", fontsize=args.fontsize)

train.py

@@ -170,7 +170,7 @@
     # HER is only a wrapper around an algo
     if args.algo == "her":
         algo_ = saved_hyperparams["model_class"]
-        assert algo_ in {"sac", "ddpg", "dqn", "td3"}, f"{algo_} is not compatible with HER"
+        assert algo_ in {"sac", "ddpg", "dqn", "td3", "tqc"}, f"{algo_} is not compatible with HER"
         # Retrieve the model class
         hyperparams["model_class"] = ALGOS[saved_hyperparams["model_class"]]
 

utils/utils.py

@@ -6,19 +6,31 @@
 
 import gym
 import yaml
-
-# from stable_baselines3.common import logger
 from stable_baselines3 import A2C, DDPG, DQN, HER, PPO, SAC, TD3
 from stable_baselines3.common.monitor import Monitor
-
-# from stable_baselines3.common.cmd_util import make_atari_env
 from stable_baselines3.common.utils import set_random_seed
 from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv, VecFrameStack, VecNormalize
 
+try:
+    from sb3_contrib import TQC  # pytype: disable=import-error
+except ImportError:
+    TQC = None
+
 # For custom activation fn
 from torch import nn as nn  # noqa: F401 pylint: disable=unused-import
 
-ALGOS = {"a2c": A2C, "ddpg": DDPG, "dqn": DQN, "her": HER, "ppo": PPO, "sac": SAC, "td3": TD3}
+ALGOS = {
+    "a2c": A2C,
+    "ddpg": DDPG,
+    "dqn": DQN,
+    "ppo": PPO,
+    "her": HER,
+    "sac": SAC,
+    "td3": TD3,
+}
+
+if TQC is not None:
+    ALGOS["tqc"] = TQC
 
 
 def flatten_dict_observations(env):

utils/wrappers.py

@@ -1,6 +1,7 @@
 import gym
 import numpy as np
 from matplotlib import pyplot as plt
+from scipy.signal import iirfilter, sosfilt, zpk2sos
 
 
 class DoneOnSuccessWrapper(gym.Wrapper):
@@ -170,9 +171,6 @@ def step(self, action):
 
 
 # from https://docs.obspy.org
-from scipy.signal import iirfilter, sosfilt, zpk2sos
-
-
 def lowpass(data, freq, df, corners=4, zerophase=False):
     """
     Butterworth-Lowpass Filter.