Add Intrinsic Curiosity Module

README.md

@@ -43,6 +43,7 @@
 - [Prioritized Experience Replay (PER)](https://arxiv.org/pdf/1511.05952.pdf)
 - [Generalized Advantage Estimator (GAE)](https://arxiv.org/pdf/1506.02438.pdf)
 - [Posterior Sampling Reinforcement Learning (PSRL)](https://www.ece.uvic.ca/~bctill/papers/learning/Strens_2000.pdf)
+- [Intrinsic Curiosity Module (ICM)](https://arxiv.org/pdf/1705.05363.pdf)
 
 Here is Tianshou's other features:
 

docs/api/tianshou.policy.rst

@@ -137,6 +137,11 @@ Model-based
    :undoc-members:
    :show-inheritance:
 
+.. autoclass:: tianshou.policy.ICMPolicy
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 Multi-agent
 -----------
 

docs/index.rst

@@ -32,6 +32,7 @@ Welcome to Tianshou!
 * :class:`~tianshou.policy.DiscreteCQLPolicy` `Discrete Conservative Q-Learning <https://arxiv.org/pdf/2006.04779.pdf>`_
 * :class:`~tianshou.policy.DiscreteCRRPolicy` `Critic Regularized Regression <https://arxiv.org/pdf/2006.15134.pdf>`_
 * :class:`~tianshou.policy.PSRLPolicy` `Posterior Sampling Reinforcement Learning <https://www.ece.uvic.ca/~bctill/papers/learning/Strens_2000.pdf>`_
+* :class:`~tianshou.policy.ICMPolicy` `Intrinsic Curiosity Module <https://arxiv.org/pdf/1705.05363.pdf>`_
 * :class:`~tianshou.data.PrioritizedReplayBuffer` `Prioritized Experience Replay <https://arxiv.org/pdf/1511.05952.pdf>`_
 * :meth:`~tianshou.policy.BasePolicy.compute_episodic_return` `Generalized Advantage Estimator <https://arxiv.org/pdf/1506.02438.pdf>`_
 

docs/spelling_wordlist.txt

@@ -135,3 +135,4 @@ Huayu
 Strens
 Ornstein
 Uhlenbeck
+mse

examples/atari/atari_dqn.py

@@ -11,8 +11,10 @@
 from tianshou.data import Collector, VectorReplayBuffer
 from tianshou.env import ShmemVectorEnv
 from tianshou.policy import DQNPolicy
+from tianshou.policy.modelbased.icm import ICMPolicy
 from tianshou.trainer import offpolicy_trainer
 from tianshou.utils import TensorboardLogger, WandbLogger
+from tianshou.utils.net.discrete import IntrinsicCuriosityModule
 
 
 def get_args():
@@ -55,6 +57,24 @@ def get_args():
         help='watch the play of pre-trained policy only'
     )
     parser.add_argument('--save-buffer-name', type=str, default=None)
+    parser.add_argument(
+        '--icm-lr-scale',
+        type=float,
+        default=0.,
+        help='use intrinsic curiosity module with this lr scale'
+    )
+    parser.add_argument(
+        '--icm-reward-scale',
+        type=float,
+        default=0.01,
+        help='scaling factor for intrinsic curiosity reward'
+    )
+    parser.add_argument(
+        '--icm-forward-loss-weight',
+        type=float,
+        default=0.2,
+        help='weight for the forward model loss in ICM'
+    )
     return parser.parse_args()
 
 
@@ -101,6 +121,24 @@ def test_dqn(args=get_args()):
         args.n_step,
         target_update_freq=args.target_update_freq
     )
+    if args.icm_lr_scale > 0:
+        feature_net = DQN(
+            *args.state_shape, args.action_shape, args.device, features_only=True
+        )
+        action_dim = np.prod(args.action_shape)
+        feature_dim = feature_net.output_dim
+        icm_net = IntrinsicCuriosityModule(
+            feature_net.net,
+            feature_dim,
+            action_dim,
+            hidden_sizes=[512],
+            device=args.device
+        )
+        icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)
+        policy = ICMPolicy(
+            policy, icm_net, icm_optim, args.icm_lr_scale, args.icm_reward_scale,
+            args.icm_forward_loss_weight
+        ).to(args.device)
     # load a previous policy
     if args.resume_path:
         policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
@@ -118,7 +156,8 @@ def test_dqn(args=get_args()):
     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, 'dqn')
+    log_name = 'dqn_icm' if args.icm_lr_scale > 0 else 'dqn'
+    log_path = os.path.join(args.logdir, args.task, log_name)
     if args.logger == "tensorboard":
         writer = SummaryWriter(log_path)
         writer.add_text("args", str(args))
@@ -127,7 +166,7 @@ def test_dqn(args=get_args()):
         logger = WandbLogger(
             save_interval=1,
             project=args.task,
-            name='dqn',
+            name=log_name,
             run_id=args.resume_id,
             config=args,
         )

examples/vizdoom/vizdoom_a2c_icm.py

@@ -0,0 +1,225 @@
+import argparse
+import os
+import pprint
+
+import numpy as np
+import torch
+from env import Env
+from network import DQN
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.data import Collector, VectorReplayBuffer
+from tianshou.env import ShmemVectorEnv
+from tianshou.policy import A2CPolicy, ICMPolicy
+from tianshou.trainer import onpolicy_trainer
+from tianshou.utils import TensorboardLogger
+from tianshou.utils.net.common import ActorCritic
+from tianshou.utils.net.discrete import Actor, Critic, IntrinsicCuriosityModule
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--task', type=str, default='D2_navigation')
+    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--buffer-size', type=int, default=2000000)
+    parser.add_argument('--lr', type=float, default=0.0001)
+    parser.add_argument('--gamma', type=float, default=0.99)
+    parser.add_argument('--epoch', type=int, default=300)
+    parser.add_argument('--step-per-epoch', type=int, default=100000)
+    parser.add_argument('--episode-per-collect', type=int, default=10)
+    parser.add_argument('--update-per-step', type=float, default=0.1)
+    parser.add_argument('--update-per-step', type=int, default=1)
+    parser.add_argument('--batch-size', type=int, default=64)
+    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[512])
+    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(
+        '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
+    )
+    parser.add_argument('--frames-stack', type=int, default=4)
+    parser.add_argument('--skip-num', 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-lmp',
+        default=False,
+        action='store_true',
+        help='save lmp file for replay whole episode'
+    )
+    parser.add_argument('--save-buffer-name', type=str, default=None)
+    parser.add_argument(
+        '--icm-lr-scale',
+        type=float,
+        default=0.,
+        help='use intrinsic curiosity module with this lr scale'
+    )
+    parser.add_argument(
+        '--icm-reward-scale',
+        type=float,
+        default=0.01,
+        help='scaling factor for intrinsic curiosity reward'
+    )
+    parser.add_argument(
+        '--icm-forward-loss-weight',
+        type=float,
+        default=0.2,
+        help='weight for the forward model loss in ICM'
+    )
+    return parser.parse_args()
+
+
+def test_a2c(args=get_args()):
+    args.cfg_path = f"maps/{args.task}.cfg"
+    args.wad_path = f"maps/{args.task}.wad"
+    args.res = (args.skip_num, 84, 84)
+    env = Env(args.cfg_path, args.frames_stack, args.res)
+    args.state_shape = args.res
+    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 = ShmemVectorEnv(
+        [
+            lambda: Env(args.cfg_path, args.frames_stack, args.res)
+            for _ in range(args.training_num)
+        ]
+    )
+    test_envs = ShmemVectorEnv(
+        [
+            lambda: Env(args.cfg_path, args.frames_stack, args.res, args.save_lmp)
+            for _ in range(min(os.cpu_count() - 1, 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
+    net = DQN(
+        *args.state_shape, args.action_shape, device=args.device, features_only=True
+    )
+    actor = Actor(
+        net, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device
+    )
+    critic = Critic(net, hidden_sizes=args.hidden_sizes, device=args.device)
+    optim = torch.optim.Adam(ActorCritic(actor, critic).parameters(), lr=args.lr)
+    # define policy
+    dist = torch.distributions.Categorical
+    policy = A2CPolicy(actor, critic, optim, dist).to(args.device)
+    if args.icm_lr_scale > 0:
+        feature_net = DQN(
+            *args.state_shape,
+            args.action_shape,
+            device=args.device,
+            features_only=True
+        )
+        action_dim = np.prod(args.action_shape)
+        feature_dim = feature_net.output_dim
+        icm_net = IntrinsicCuriosityModule(
+            feature_net.net,
+            feature_dim,
+            action_dim,
+            hidden_sizes=args.hidden_sizes,
+            device=args.device
+        )
+        icm_optim = torch.optim.adam(icm_net.parameters(), lr=args.lr)
+        policy = ICMPolicy(
+            policy, icm_net, icm_optim, args.icm_lr_scale, args.icm_reward_scale,
+            args.icm_forward_loss_weight
+        ).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, 'a2c')
+    writer = SummaryWriter(log_path)
+    writer.add_text("args", str(args))
+    logger = TensorboardLogger(writer)
+
+    def save_fn(policy):
+        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
+
+    def stop_fn(mean_rewards):
+        return False
+
+    def watch():
+        # watch agent's performance
+        print("Setup test envs ...")
+        policy.eval()
+        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()
+        lens = result["lens"].mean() * args.skip_num
+        print(f'Mean reward (over {result["n/ep"]} episodes): {rew}')
+        print(f'Mean length (over {result["n/ep"]} episodes): {lens}')
+
+    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 = onpolicy_trainer(
+        policy,
+        train_collector,
+        test_collector,
+        args.epoch,
+        args.step_per_epoch,
+        args.repeat_per_collect,
+        args.test_num,
+        args.batch_size,
+        episode_per_collect=args.episode_per_collect,
+        stop_fn=stop_fn,
+        save_fn=save_fn,
+        logger=logger,
+        test_in_train=False
+    )
+
+    pprint.pprint(result)
+    watch()
+
+
+if __name__ == '__main__':
+    test_a2c(get_args())