thu-ml · Trinkle23897 · Jun 15, 2021 · May 23, 2021 · May 31, 2021 · Jun 1, 2021
diff --git a/README.md b/README.md
@@ -25,6 +25,7 @@
 - [Categorical DQN (C51)](https://arxiv.org/pdf/1707.06887.pdf)
 - [Quantile Regression DQN (QRDQN)](https://arxiv.org/pdf/1710.10044.pdf)
 - [Implicit Quantile Network (IQN)](https://arxiv.org/pdf/1806.06923.pdf)
+- [Fully-parameterized Quantile Function (FQF)](https://arxiv.org/pdf/1911.02140.pdf)
 - [Policy Gradient (PG)](https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf)
 - [Natural Policy Gradient (NPG)](https://proceedings.neurips.cc/paper/2001/file/4b86abe48d358ecf194c56c69108433e-Paper.pdf)
 - [Advantage Actor-Critic (A2C)](https://openai.com/blog/baselines-acktr-a2c/)

diff --git a/docs/api/tianshou.policy.rst b/docs/api/tianshou.policy.rst
@@ -40,6 +40,11 @@ DQN Family
    :undoc-members:
    :show-inheritance:
 
+.. autoclass:: tianshou.policy.FQFPolicy
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 On-policy
 ~~~~~~~~~
 

diff --git a/docs/index.rst b/docs/index.rst
@@ -15,6 +15,7 @@ Welcome to Tianshou!
 * :class:`~tianshou.policy.C51Policy` `Categorical DQN <https://arxiv.org/pdf/1707.06887.pdf>`_
 * :class:`~tianshou.policy.QRDQNPolicy` `Quantile Regression DQN <https://arxiv.org/pdf/1710.10044.pdf>`_
 * :class:`~tianshou.policy.IQNPolicy` `Implicit Quantile Network <https://arxiv.org/pdf/1806.06923.pdf>`_
+* :class:`~tianshou.policy.FQFPolicy` `Fully-parameterized Quantile Function <https://arxiv.org/pdf/1911.02140.pdf>`_
 * :class:`~tianshou.policy.PGPolicy` `Policy Gradient <https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf>`_
 * :class:`~tianshou.policy.NPGPolicy` `Natural Policy Gradient <https://proceedings.neurips.cc/paper/2001/file/4b86abe48d358ecf194c56c69108433e-Paper.pdf>`_
 * :class:`~tianshou.policy.A2CPolicy` `Advantage Actor-Critic <https://openai.com/blog/baselines-acktr-a2c/>`_

diff --git a/examples/atari/README.md b/examples/atari/README.md
@@ -68,6 +68,20 @@ One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.
 | SeaquestNoFrameskip-v4      | 4874        | ![](results/iqn/Seaquest_rew.png)     | `python3 atari_iqn.py --task "SeaquestNoFrameskip-v4"`  |
 | SpaceInvadersNoFrameskip-v4 | 1498.5      | ![](results/iqn/SpaceInvaders_rew.png) | `python3 atari_iqn.py --task "SpaceInvadersNoFrameskip-v4"`  |
 
+# FQF (single run)
+
+One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.
+
+| task                        | best reward | reward curve                          | parameters                                                   |
+| --------------------------- | ----------- | ------------------------------------- | ------------------------------------------------------------ |
+| PongNoFrameskip-v4          | 20.7        | ![](results/fqf/Pong_rew.png)         | `python3 atari_fqf.py --task "PongNoFrameskip-v4" --batch-size 64` |
+| BreakoutNoFrameskip-v4      | 517.3       | ![](results/fqf/Breakout_rew.png)     | `python3 atari_fqf.py --task "BreakoutNoFrameskip-v4" --n-step 1` |
+| EnduroNoFrameskip-v4        | 2240.5      | ![](results/fqf/Enduro_rew.png)       | `python3 atari_fqf.py --task "EnduroNoFrameskip-v4"`  |
+| QbertNoFrameskip-v4         | 16172.5     | ![](results/fqf/Qbert_rew.png)        | `python3 atari_fqf.py --task "QbertNoFrameskip-v4"`  |
+| MsPacmanNoFrameskip-v4      | 2429        | ![](results/fqf/MsPacman_rew.png)     | `python3 atari_fqf.py --task "MsPacmanNoFrameskip-v4"`  |
+| SeaquestNoFrameskip-v4      | 10775       | ![](results/fqf/Seaquest_rew.png)     | `python3 atari_fqf.py --task "SeaquestNoFrameskip-v4"`  |
+| SpaceInvadersNoFrameskip-v4 | 2482        | ![](results/fqf/SpaceInvaders_rew.png) | `python3 atari_fqf.py --task "SpaceInvadersNoFrameskip-v4"`  |
+
 # BCQ
 
 To running BCQ algorithm on Atari, you need to do the following things:

diff --git a/examples/atari/atari_fqf.py b/examples/atari/atari_fqf.py
@@ -0,0 +1,186 @@
+import os
+import torch
+import pprint
+import argparse
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.policy import FQFPolicy
+from tianshou.utils import BasicLogger
+from tianshou.env import SubprocVectorEnv
+from tianshou.trainer import offpolicy_trainer
+from tianshou.data import Collector, VectorReplayBuffer
+from tianshou.utils.net.discrete import FractionProposalNetwork, FullQuantileFunction
+
+from atari_network import DQN
+from atari_wrapper import wrap_deepmind
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--task', type=str, default='PongNoFrameskip-v4')
+    parser.add_argument('--seed', type=int, default=3128)
+    parser.add_argument('--eps-test', type=float, default=0.005)
+    parser.add_argument('--eps-train', type=float, default=1.)
+    parser.add_argument('--eps-train-final', type=float, default=0.05)
+    parser.add_argument('--buffer-size', type=int, default=100000)
+    parser.add_argument('--lr', type=float, default=5e-5)
+    parser.add_argument('--fraction-lr', type=float, default=2.5e-9)
+    parser.add_argument('--gamma', type=float, default=0.99)
+    parser.add_argument('--num-fractions', type=int, default=32)
+    parser.add_argument('--num-cosines', type=int, default=64)
+    parser.add_argument('--ent-coef', type=float, default=10.)
+    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[512])
+    parser.add_argument('--n-step', type=int, default=3)
+    parser.add_argument('--target-update-freq', type=int, default=500)
+    parser.add_argument('--epoch', type=int, default=100)
+    parser.add_argument('--step-per-epoch', type=int, default=100000)
+    parser.add_argument('--step-per-collect', type=int, default=10)
+    parser.add_argument('--update-per-step', type=float, default=0.1)
+    parser.add_argument('--batch-size', type=int, default=32)
+    parser.add_argument('--training-num', type=int, default=10)
+    parser.add_argument('--test-num', type=int, default=10)
+    parser.add_argument('--logdir', type=str, default='log')
+    parser.add_argument('--render', type=float, default=0.)
+    parser.add_argument(
+        '--device', type=str,
+        default='cuda' if torch.cuda.is_available() else 'cpu')
+    parser.add_argument('--frames-stack', type=int, default=4)
+    parser.add_argument('--resume-path', type=str, default=None)
+    parser.add_argument('--watch', default=False, action='store_true',
+                        help='watch the play of pre-trained policy only')
+    parser.add_argument('--save-buffer-name', type=str, default=None)
+    return parser.parse_args()
+
+
+def make_atari_env(args):
+    return wrap_deepmind(args.task, frame_stack=args.frames_stack)
+
+
+def make_atari_env_watch(args):
+    return wrap_deepmind(args.task, frame_stack=args.frames_stack,
+                         episode_life=False, clip_rewards=False)
+
+
+def test_fqf(args=get_args()):
+    env = make_atari_env(args)
+    args.state_shape = env.observation_space.shape or env.observation_space.n
+    args.action_shape = env.action_space.shape or env.action_space.n
+    # should be N_FRAMES x H x W
+    print("Observations shape:", args.state_shape)
+    print("Actions shape:", args.action_shape)
+    # make environments
+    train_envs = SubprocVectorEnv([lambda: make_atari_env(args)
+                                   for _ in range(args.training_num)])
+    test_envs = SubprocVectorEnv([lambda: make_atari_env_watch(args)
+                                  for _ in range(args.test_num)])
+    # seed
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    train_envs.seed(args.seed)
+    test_envs.seed(args.seed)
+    # define model
+    feature_net = DQN(*args.state_shape, args.action_shape, args.device,
+                      features_only=True)
+    net = FullQuantileFunction(
+        feature_net, args.action_shape, args.hidden_sizes,
+        args.num_cosines, device=args.device
+    ).to(args.device)
+    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
+    fraction_net = FractionProposalNetwork(args.num_fractions, net.input_dim)
+    fraction_optim = torch.optim.RMSprop(fraction_net.parameters(),
+                                         lr=args.fraction_lr)
+    # define policy
+    policy = FQFPolicy(
+        net, optim, fraction_net, fraction_optim,
+        args.gamma, args.num_fractions, args.ent_coef, args.n_step,
+        target_update_freq=args.target_update_freq
+    ).to(args.device)
+    # load a previous policy
+    if args.resume_path:
+        policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
+        print("Loaded agent from: ", args.resume_path)
+    # replay buffer: `save_last_obs` and `stack_num` can be removed together
+    # when you have enough RAM
+    buffer = VectorReplayBuffer(
+        args.buffer_size, buffer_num=len(train_envs),
+        ignore_obs_next=True, save_only_last_obs=True, stack_num=args.frames_stack)
+    # collector
+    train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
+    test_collector = Collector(policy, test_envs, exploration_noise=True)
+    # log
+    log_path = os.path.join(args.logdir, args.task, 'fqf')
+    writer = SummaryWriter(log_path)
+    writer.add_text("args", str(args))
+    logger = BasicLogger(writer)
+
+    def save_fn(policy):
+        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
+
+    def stop_fn(mean_rewards):
+        if env.spec.reward_threshold:
+            return mean_rewards >= env.spec.reward_threshold
+        elif 'Pong' in args.task:
+            return mean_rewards >= 20
+        else:
+            return False
+
+    def train_fn(epoch, env_step):
+        # nature DQN setting, linear decay in the first 1M steps
+        if env_step <= 1e6:
+            eps = args.eps_train - env_step / 1e6 * \
+                (args.eps_train - args.eps_train_final)
+        else:
+            eps = args.eps_train_final
+        policy.set_eps(eps)
+        logger.write('train/eps', env_step, eps)
+
+    def test_fn(epoch, env_step):
+        policy.set_eps(args.eps_test)
+
+    # watch agent's performance
+    def watch():
+        print("Setup test envs ...")
+        policy.eval()
+        policy.set_eps(args.eps_test)
+        test_envs.seed(args.seed)
+        if args.save_buffer_name:
+            print(f"Generate buffer with size {args.buffer_size}")
+            buffer = VectorReplayBuffer(
+                args.buffer_size, buffer_num=len(test_envs),
+                ignore_obs_next=True, save_only_last_obs=True,
+                stack_num=args.frames_stack)
+            collector = Collector(policy, test_envs, buffer,
+                                  exploration_noise=True)
+            result = collector.collect(n_step=args.buffer_size)
+            print(f"Save buffer into {args.save_buffer_name}")
+            # Unfortunately, pickle will cause oom with 1M buffer size
+            buffer.save_hdf5(args.save_buffer_name)
+        else:
+            print("Testing agent ...")
+            test_collector.reset()
+            result = test_collector.collect(n_episode=args.test_num,
+                                            render=args.render)
+        rew = result["rews"].mean()
+        print(f'Mean reward (over {result["n/ep"]} episodes): {rew}')
+
+    if args.watch:
+        watch()
+        exit(0)
+
+    # test train_collector and start filling replay buffer
+    train_collector.collect(n_step=args.batch_size * args.training_num)
+    # trainer
+    result = offpolicy_trainer(
+        policy, train_collector, test_collector, args.epoch,
+        args.step_per_epoch, args.step_per_collect, args.test_num,
+        args.batch_size, train_fn=train_fn, test_fn=test_fn,
+        stop_fn=stop_fn, save_fn=save_fn, logger=logger,
+        update_per_step=args.update_per_step, test_in_train=False)
+
+    pprint.pprint(result)
+    watch()
+
+
+if __name__ == '__main__':
+    test_fqf(get_args())
diff --git a/examples/atari/results/fqf/Breakout_rew.png b/examples/atari/results/fqf/Breakout_rew.png
diff --git a/examples/atari/results/fqf/Enduro_rew.png b/examples/atari/results/fqf/Enduro_rew.png
diff --git a/examples/atari/results/fqf/MsPacman_rew.png b/examples/atari/results/fqf/MsPacman_rew.png
diff --git a/examples/atari/results/fqf/Pong_rew.png b/examples/atari/results/fqf/Pong_rew.png
diff --git a/examples/atari/results/fqf/Qbert_rew.png b/examples/atari/results/fqf/Qbert_rew.png
diff --git a/examples/atari/results/fqf/Seaquest_rew.png b/examples/atari/results/fqf/Seaquest_rew.png
diff --git a/examples/atari/results/fqf/SpaceInvaders_rew.png b/examples/atari/results/fqf/SpaceInvaders_rew.png
diff --git a/test/discrete/test_fqf.py b/test/discrete/test_fqf.py
@@ -0,0 +1,153 @@
+import os
+import gym
+import torch
+import pprint
+import argparse
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.policy import FQFPolicy
+from tianshou.utils import BasicLogger
+from tianshou.env import DummyVectorEnv
+from tianshou.utils.net.common import Net
+from tianshou.trainer import offpolicy_trainer
+from tianshou.utils.net.discrete import FractionProposalNetwork, FullQuantileFunction
+from tianshou.data import Collector, VectorReplayBuffer, PrioritizedVectorReplayBuffer
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--task', type=str, default='CartPole-v0')
+    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--eps-test', type=float, default=0.05)
+    parser.add_argument('--eps-train', type=float, default=0.1)
+    parser.add_argument('--buffer-size', type=int, default=20000)
+    parser.add_argument('--lr', type=float, default=3e-3)
+    parser.add_argument('--fraction-lr', type=float, default=2.5e-9)
+    parser.add_argument('--gamma', type=float, default=0.9)
+    parser.add_argument('--num-fractions', type=int, default=32)
+    parser.add_argument('--num-cosines', type=int, default=64)
+    parser.add_argument('--ent-coef', type=float, default=10.)
+    parser.add_argument('--n-step', type=int, default=3)
+    parser.add_argument('--target-update-freq', type=int, default=320)
+    parser.add_argument('--epoch', type=int, default=10)
+    parser.add_argument('--step-per-epoch', type=int, default=10000)
+    parser.add_argument('--step-per-collect', type=int, default=10)
+    parser.add_argument('--update-per-step', type=float, default=0.1)
+    parser.add_argument('--batch-size', type=int, default=64)
+    parser.add_argument('--hidden-sizes', type=int,
+                        nargs='*', default=[64, 64, 64])
+    parser.add_argument('--training-num', type=int, default=10)
+    parser.add_argument('--test-num', type=int, default=100)
+    parser.add_argument('--logdir', type=str, default='log')
+    parser.add_argument('--render', type=float, default=0.)
+    parser.add_argument('--prioritized-replay',
+                        action="store_true", default=False)
+    parser.add_argument('--alpha', type=float, default=0.6)
+    parser.add_argument('--beta', type=float, default=0.4)
+    parser.add_argument(
+        '--device', type=str,
+        default='cuda' if torch.cuda.is_available() else 'cpu')
+    args = parser.parse_known_args()[0]
+    return args
+
+
+def test_fqf(args=get_args()):
+    env = gym.make(args.task)
+    args.state_shape = env.observation_space.shape or env.observation_space.n
+    args.action_shape = env.action_space.shape or env.action_space.n
+    # train_envs = gym.make(args.task)
+    # you can also use tianshou.env.SubprocVectorEnv
+    train_envs = DummyVectorEnv(
+        [lambda: gym.make(args.task) for _ in range(args.training_num)])
+    # test_envs = gym.make(args.task)
+    test_envs = DummyVectorEnv(
+        [lambda: gym.make(args.task) for _ in range(args.test_num)])
+    # seed
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    train_envs.seed(args.seed)
+    test_envs.seed(args.seed)
+    # model
+    feature_net = Net(args.state_shape, args.hidden_sizes[-1],
+                      hidden_sizes=args.hidden_sizes[:-1], device=args.device,
+                      softmax=False)
+    net = FullQuantileFunction(
+        feature_net, args.action_shape, args.hidden_sizes,
+        num_cosines=args.num_cosines, device=args.device
+    )
+    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
+    fraction_net = FractionProposalNetwork(args.num_fractions, net.input_dim)
+    fraction_optim = torch.optim.RMSprop(
+        fraction_net.parameters(), lr=args.fraction_lr
+    )
+    policy = FQFPolicy(
+        net, optim, fraction_net, fraction_optim, args.gamma, args.num_fractions,
+        args.ent_coef, args.n_step, target_update_freq=args.target_update_freq
+    ).to(args.device)
+    # buffer
+    if args.prioritized_replay:
+        buf = PrioritizedVectorReplayBuffer(
+            args.buffer_size, buffer_num=len(train_envs),
+            alpha=args.alpha, beta=args.beta)
+    else:
+        buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
+    # collector
+    train_collector = Collector(policy, train_envs, buf, exploration_noise=True)
+    test_collector = Collector(policy, test_envs, exploration_noise=True)
+    # policy.set_eps(1)
+    train_collector.collect(n_step=args.batch_size * args.training_num)
+    # log
+    log_path = os.path.join(args.logdir, args.task, 'fqf')
+    writer = SummaryWriter(log_path)
+    logger = BasicLogger(writer)
+
+    def save_fn(policy):
+        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
+
+    def stop_fn(mean_rewards):
+        return mean_rewards >= env.spec.reward_threshold
+
+    def train_fn(epoch, env_step):
+        # eps annnealing, just a demo
+        if env_step <= 10000:
+            policy.set_eps(args.eps_train)
+        elif env_step <= 50000:
+            eps = args.eps_train - (env_step - 10000) / \
+                40000 * (0.9 * args.eps_train)
+            policy.set_eps(eps)
+        else:
+            policy.set_eps(0.1 * args.eps_train)
+
+    def test_fn(epoch, env_step):
+        policy.set_eps(args.eps_test)
+
+    # trainer
+    result = offpolicy_trainer(
+        policy, train_collector, test_collector, args.epoch,
+        args.step_per_epoch, args.step_per_collect, args.test_num,
+        args.batch_size, train_fn=train_fn, test_fn=test_fn,
+        stop_fn=stop_fn, save_fn=save_fn, logger=logger,
+        update_per_step=args.update_per_step)
+    assert stop_fn(result['best_reward'])
+
+    if __name__ == '__main__':
+        pprint.pprint(result)
+        # Let's watch its performance!
+        env = gym.make(args.task)
+        policy.eval()
+        policy.set_eps(args.eps_test)
+        collector = Collector(policy, env)
+        result = collector.collect(n_episode=1, render=args.render)
+        rews, lens = result["rews"], result["lens"]
+        print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
+
+
+def test_pfqf(args=get_args()):
+    args.prioritized_replay = True
+    args.gamma = .95
+    test_fqf(args)
+
+
+if __name__ == '__main__':
+    test_fqf(get_args())