Random Network Distillation for Torch

com.unity.ml-agents/CHANGELOG.md

@@ -10,6 +10,9 @@ and this project adheres to
 ### Major Changes
 #### com.unity.ml-agents (C#)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
+ - Added the Random Network Distillation (RND) intrinsic reward signal to the Pytorch
+ trainers. To use RND, add a `rnd` section to the `reward_signals` section of your
+ yaml configuration file. [More information here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-Configuration-File.md#rnd-intrinsic-reward)
 
 ### Minor Changes
 #### com.unity.ml-agents (C#)

config/ppo/PyramidsRND.yaml

@@ -0,0 +1,32 @@
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      rnd:
+        gamma: 0.99
+        strength: 0.01
+        encoding_size: 64
+        learning_rate: 0.0001
+    keep_checkpoints: 5
+    max_steps: 3000000
+    time_horizon: 128
+    summary_freq: 30000
+    framework: pytorch
+    threaded: true

docs/ML-Agents-Overview.md

@@ -13,6 +13,7 @@
   - [A Quick Note on Reward Signals](#a-quick-note-on-reward-signals)
   - [Deep Reinforcement Learning](#deep-reinforcement-learning)
     - [Curiosity for Sparse-reward Environments](#curiosity-for-sparse-reward-environments)
+    - [RND for Sparse-reward Environments](#rnd-for-sparse-reward-environments)
   - [Imitation Learning](#imitation-learning)
     - [GAIL (Generative Adversarial Imitation Learning)](#gail-generative-adversarial-imitation-learning)
     - [Behavioral Cloning (BC)](#behavioral-cloning-bc)
@@ -359,7 +360,7 @@ The total reward that the agent will learn to maximize can be a mix of extrinsic
 and intrinsic reward signals.
 
 The ML-Agents Toolkit allows reward signals to be defined in a modular way, and
-we provide three reward signals that can the mixed and matched to help shape
+we provide four reward signals that can the mixed and matched to help shape
 your agent's behavior:
 
 - `extrinsic`: represents the rewards defined in your environment, and is
@@ -369,6 +370,9 @@ your agent's behavior:
 - `curiosity`: represents an intrinsic reward signal that encourages exploration
   in sparse-reward environments that is defined by the Curiosity module (see
   below).
+- `rnd`: represents an intrinsic reward signal that encourages exploration
+  in sparse-reward environments that is defined by the Curiosity module (see
+  below). (Not available for TensorFlow trainers)
 
 ### Deep Reinforcement Learning
 
@@ -417,6 +421,24 @@ model is, the larger the reward will be.
 For more information, see our dedicated
 [blog post on the Curiosity module](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/).
 
+#### RND for Sparse-reward Environments
+
+Similarly to Curiosity, Random Network Distillation (RND) is useful in sparse or rare
+reward environments as it helps the Agent explore. The RND Module is implemented following
+the paper [Exploration by Random Network Distillation](https://arxiv.org/abs/1810.12894).
+RND uses two networks:
+ - The first is a network with fixed random weights that takes observations as inputs and
+ generates an encoding
+ - The second is a network with similar architecture that is trained to predict the
+ outputs of the first network and uses the observations the Agent collects as training data.
+
+The loss (the squared difference between the predicted and actual encoded observations)
+of the trained model is used as intrinsic reward. The more an Agent visits a state, the
+more accurate the predictions and the lower the rewards which encourages the Agent to
+explore new states with higher prediction errors.
+
+__Note:__ RND is not available for TensorFlow trainers (only PyTorch trainers)
+
 ### Imitation Learning
 
 It is often more intuitive to simply demonstrate the behavior we want an agent

docs/Training-Configuration-File.md

@@ -10,6 +10,7 @@
   - [Extrinsic Rewards](#extrinsic-rewards)
   - [Curiosity Intrinsic Reward](#curiosity-intrinsic-reward)
   - [GAIL Intrinsic Reward](#gail-intrinsic-reward)
+  - [RND Intrinsic Reward](#rnd-intrinsic-reward)
   - [Reward Signal Settings for SAC](#reward-signal-settings-for-sac)
 - [Behavioral Cloning](#behavioral-cloning)
 - [Memory-enhanced Agents using Recurrent Neural Networks](#memory-enhanced-agents-using-recurrent-neural-networks)
@@ -118,6 +119,18 @@ settings:
 | `gail -> use_actions`   | (default = `false`) Determines whether the discriminator should discriminate based on both observations and actions, or just observations. Set to True if you want the agent to mimic the actions from the demonstrations, and False if you'd rather have the agent visit the same states as in the demonstrations but with possibly different actions. Setting to False is more likely to be stable, especially with imperfect demonstrations, but may learn slower. |
 | `gail -> use_vail`      | (default = `false`) Enables a variational bottleneck within the GAIL discriminator. This forces the discriminator to learn a more general representation and reduces its tendency to be "too good" at discriminating, making learning more stable. However, it does increase training time. Enable this if you notice your imitation learning is unstable, or unable to learn the task at hand.                                                                       |
 
+### RND Intrinsic Reward
+
+Random Network Distillation (RND) is only available for the PyTorch trainers.
+To enable RND, provide these settings:
+
+| **Setting**                  | **Description**                                                                                                                                                                                                                                                                                                                       |
+| :--------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `rnd -> strength`      | (default = `1.0`) Magnitude of the curiosity reward generated by the intrinsic rnd module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. <br><br>Typical range: `0.001` - `0.01` |
+| `rnd -> gamma`         | (default = `0.99`) Discount factor for future rewards. <br><br>Typical range: `0.8` - `0.995`                                                                                                                                                                                                                                                            |
+| `rnd -> encoding_size` | (default = `64`) Size of the encoding used by the intrinsic RND model. <br><br>Typical range: `64` - `256` |
+| `curiosity -> learning_rate` | (default = `3e-4`) Learning rate used to update the RND module. This should be large enough for the RND module to quickly learn the state representation, but small enough to allow for stable learning. <br><br>Typical range: `1e-5` - `1e-3`
+
 
 ## Behavioral Cloning
 

ml-agents/mlagents/trainers/model_saver/tf_model_saver.py

@@ -74,6 +74,9 @@ def export(self, output_filepath: str, behavior_name: str) -> None:
         # only on worker-0 if there are multiple workers
         if self.policy and self.policy.rank is not None and self.policy.rank != 0:
             return
+        if self.graph is None:
+            logger.info("No model to export")
+            return
         export_policy_model(
             self.model_path, output_filepath, behavior_name, self.graph, self.sess
         )

ml-agents/mlagents/trainers/settings.py

@@ -167,12 +167,14 @@ class RewardSignalType(Enum):
     EXTRINSIC: str = "extrinsic"
     GAIL: str = "gail"
     CURIOSITY: str = "curiosity"
+    RND: str = "rnd"
 
     def to_settings(self) -> type:
         _mapping = {
             RewardSignalType.EXTRINSIC: RewardSignalSettings,
             RewardSignalType.GAIL: GAILSettings,
             RewardSignalType.CURIOSITY: CuriositySettings,
+            RewardSignalType.RND: RNDSettings,
         }
         return _mapping[self]
 
@@ -214,6 +216,12 @@ class CuriositySettings(RewardSignalSettings):
     learning_rate: float = 3e-4
 
 
+@attr.s(auto_attribs=True)
+class RNDSettings(RewardSignalSettings):
+    encoding_size: int = 64
+    learning_rate: float = 1e-4
+
+
 # SAMPLERS #############################################################################
 class ParameterRandomizationType(Enum):
     UNIFORM: str = "uniform"

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_rnd.py

@@ -0,0 +1,69 @@
+import numpy as np
+import pytest
+from mlagents.torch_utils import torch
+from mlagents.trainers.torch.components.reward_providers import (
+    RNDRewardProvider,
+    create_reward_provider,
+)
+from mlagents_envs.base_env import BehaviorSpec, ActionType
+from mlagents.trainers.settings import RNDSettings, RewardSignalType
+from mlagents.trainers.tests.torch.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+
+SEED = [42]
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3)),
+    ],
+)
+def test_construction(behavior_spec: BehaviorSpec) -> None:
+    curiosity_settings = RNDSettings(32, 0.01)
+    curiosity_settings.strength = 0.1
+    curiosity_rp = RNDRewardProvider(behavior_spec, curiosity_settings)
+    assert curiosity_rp.strength == 0.1
+    assert curiosity_rp.name == "RND"
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,), (64, 66, 3), (84, 86, 1)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,), (64, 66, 1)], ActionType.DISCRETE, (2, 3)),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2,)),
+    ],
+)
+def test_factory(behavior_spec: BehaviorSpec) -> None:
+    curiosity_settings = RNDSettings(32, 0.01)
+    curiosity_rp = create_reward_provider(
+        RewardSignalType.RND, behavior_spec, curiosity_settings
+    )
+    assert curiosity_rp.name == "RND"
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,), (64, 66, 3), (24, 26, 1)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3)),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2,)),
+    ],
+)
+def test_reward_decreases(behavior_spec: BehaviorSpec, seed: int) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    rnd_settings = RNDSettings(32, 0.01)
+    rnd_rp = RNDRewardProvider(behavior_spec, rnd_settings)
+    buffer = create_agent_buffer(behavior_spec, 5)
+    rnd_rp.update(buffer)
+    reward_old = rnd_rp.evaluate(buffer)[0]
+    for _ in range(100):
+        rnd_rp.update(buffer)
+        reward_new = rnd_rp.evaluate(buffer)[0]
+    assert reward_new < reward_old

ml-agents/mlagents/trainers/torch/components/reward_providers/__init__.py

@@ -10,6 +10,9 @@
 from mlagents.trainers.torch.components.reward_providers.gail_reward_provider import (  # noqa F401
     GAILRewardProvider,
 )
+from mlagents.trainers.torch.components.reward_providers.rnd_reward_provider import (  # noqa F401
+    RNDRewardProvider,
+)
 from mlagents.trainers.torch.components.reward_providers.reward_provider_factory import (  # noqa F401
     create_reward_provider,
 )

ml-agents/mlagents/trainers/torch/components/reward_providers/reward_provider_factory.py

@@ -15,13 +15,17 @@
 from mlagents.trainers.torch.components.reward_providers.gail_reward_provider import (
     GAILRewardProvider,
 )
+from mlagents.trainers.torch.components.reward_providers.rnd_reward_provider import (
+    RNDRewardProvider,
+)
 
 from mlagents_envs.base_env import BehaviorSpec
 
 NAME_TO_CLASS: Dict[RewardSignalType, Type[BaseRewardProvider]] = {
     RewardSignalType.EXTRINSIC: ExtrinsicRewardProvider,
     RewardSignalType.CURIOSITY: CuriosityRewardProvider,
     RewardSignalType.GAIL: GAILRewardProvider,
+    RewardSignalType.RND: RNDRewardProvider,
 }
 
 

ml-agents/mlagents/trainers/torch/components/reward_providers/rnd_reward_provider.py

@@ -0,0 +1,78 @@
+import numpy as np
+from typing import Dict
+from mlagents.torch_utils import torch
+
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.torch.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents.trainers.settings import RNDSettings
+
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.torch.utils import ModelUtils
+from mlagents.trainers.torch.networks import NetworkBody
+from mlagents.trainers.settings import NetworkSettings, EncoderType
+
+
+class RNDRewardProvider(BaseRewardProvider):
+    """
+    Implementation of Random Network Distillation : https://arxiv.org/pdf/1810.12894.pdf
+    """
+
+    def __init__(self, specs: BehaviorSpec, settings: RNDSettings) -> None:
+        super().__init__(specs, settings)
+        self._ignore_done = True
+        self._random_network = RNDNetwork(specs, settings)
+        self._training_network = RNDNetwork(specs, settings)
+        self.optimizer = torch.optim.Adam(
+            self._training_network.parameters(), lr=settings.learning_rate
+        )
+
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        with torch.no_grad():
+            target = self._random_network(mini_batch)
+            prediction = self._training_network(mini_batch)
+            rewards = torch.sum((prediction - target) ** 2, dim=1)
+        return rewards.detach().cpu().numpy()
+
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        with torch.no_grad():
+            target = self._random_network(mini_batch)
+        prediction = self._training_network(mini_batch)
+        loss = torch.mean(torch.sum((prediction - target) ** 2, dim=1))
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        return {"Losses/RND Loss": loss.detach().cpu().numpy()}
+
+
+class RNDNetwork(torch.nn.Module):
+    EPSILON = 1e-10
+
+    def __init__(self, specs: BehaviorSpec, settings: RNDSettings) -> None:
+        super().__init__()
+        self._policy_specs = specs
+        state_encoder_settings = NetworkSettings(
+            normalize=True,
+            hidden_units=settings.encoding_size,
+            num_layers=3,
+            vis_encode_type=EncoderType.SIMPLE,
+            memory=None,
+        )
+        self._encoder = NetworkBody(specs.observation_shapes, state_encoder_settings)
+
+    def forward(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        n_vis = len(self._encoder.visual_processors)
+        hidden, _ = self._encoder.forward(
+            vec_inputs=[
+                ModelUtils.list_to_tensor(mini_batch["vector_obs"], dtype=torch.float)
+            ],
+            vis_inputs=[
+                ModelUtils.list_to_tensor(
+                    mini_batch["visual_obs%d" % i], dtype=torch.float
+                )
+                for i in range(n_vis)
+            ],
+        )
+        self._encoder.update_normalization(torch.tensor(mini_batch["vector_obs"]))
+        return hidden