Enhancement/train batch function

.github/workflows/ci-testing.yml

@@ -29,7 +29,7 @@ jobs:
         #     requires: 'minimal'
 
     # Timeout: https://stackoverflow.com/a/59076067/4521646
-    timeout-minutes: 20
+    timeout-minutes: 35
 
     steps:
     - uses: actions/checkout@v2

CHANGELOG.md

@@ -45,6 +45,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Device is no longer set in the DQN model init
 - Moved RL loss function to the losses module
+- Moved rl.common.experience to datamodules
+- train_batch function to VPG model to generate batch of data at each step (POC)
+- Experience source no longer gets initialized with a device, instead the device is passed at each step()
 
 ### Fixed
 

docs/requirements.txt

@@ -6,7 +6,7 @@ pandoc
 docutils
 sphinxcontrib-fulltoc
 sphinxcontrib-mockautodoc
-gym
+
 git+https://github.com/PytorchLightning/lightning_sphinx_theme.git
 # pip_shims
 sphinx-autodoc-typehints

pl_bolts/datamodules/__init__.py

@@ -6,3 +6,5 @@
 from pl_bolts.datamodules.ssl_imagenet_datamodule import SSLImagenetDataModule
 from pl_bolts.datamodules.stl10_datamodule import STL10DataModule
 from pl_bolts.datamodules.fashion_mnist_datamodule import FashionMNISTDataModule
+from pl_bolts.datamodules.experience_source import (ExperienceSourceDataset, ExperienceSource,
+                                                    NStepExperienceSource, EpisodicExperienceStream)

pl_bolts/datamodules/experience_source.py

@@ -0,0 +1,202 @@
+"""
+Datamodules for RL models that rely on experiences generated during training
+
+Based on implementations found here: https://github.com/Shmuma/ptan/blob/master/ptan/experience.py
+"""
+from collections import deque, namedtuple
+from typing import Iterable, Callable, Tuple, List
+import numpy as np
+import torch
+from torch.utils.data import IterableDataset
+
+# Datasets
+
+Experience = namedtuple(
+    "Experience", field_names=["state", "action", "reward", "done", "new_state"]
+)
+
+
+class ExperienceSourceDataset(IterableDataset):
+    """
+    Basic experience source dataset. Takes a generate_batch function that returns an iterator.
+    The logic for the experience source and how the batch is generated is defined the Lightning model itself
+    """
+
+    def __init__(self, generate_batch: Callable):
+        self.generate_batch = generate_batch
+
+    def __iter__(self) -> Iterable:
+        iterator = self.generate_batch()
+        return iterator
+
+# Experience Sources
+
+
+class ExperienceSource(object):
+    """
+    Basic single step experience source
+
+    Args:
+        env: Environment that is being used
+        agent: Agent being used to make decisions
+    """
+
+    def __init__(self, env, agent):
+        self.env = env
+        self.agent = agent
+        self.state = self.env.reset()
+
+    def _reset(self) -> None:
+        """resets the env and state"""
+        self.state = self.env.reset()
+
+    def step(self, device: torch.device) -> Tuple[Experience, float, bool]:
+        """Takes a single step through the environment"""
+        action = self.agent(self.state, device)
+        new_state, reward, done, _ = self.env.step(action)
+        experience = Experience(
+            state=self.state,
+            action=action,
+            reward=reward,
+            new_state=new_state,
+            done=done,
+        )
+        self.state = new_state
+
+        if done:
+            self.state = self.env.reset()
+
+        return experience, reward, done
+
+    def run_episode(self, device: torch.device) -> float:
+        """Carries out a single episode and returns the total reward. This is used for testing"""
+        done = False
+        total_reward = 0
+
+        while not done:
+            _, reward, done = self.step(device)
+            total_reward += reward
+
+        return total_reward
+
+
+class NStepExperienceSource(ExperienceSource):
+    """Expands upon the basic ExperienceSource by collecting experience across N steps"""
+
+    def __init__(self, env, agent, n_steps: int = 1, gamma: float = 0.99):
+        super().__init__(env, agent)
+        self.gamma = gamma
+        self.n_steps = n_steps
+        self.n_step_buffer = deque(maxlen=n_steps)
+
+    def step(self, device: torch.device) -> Tuple[Experience, float, bool]:
+        """
+        Takes an n-step in the environment
+
+        Returns:
+            Experience
+        """
+        exp = self.n_step(device)
+
+        while len(self.n_step_buffer) < self.n_steps:
+            self.n_step(device)
+
+        reward, next_state, done = self.get_transition_info()
+        first_experience = self.n_step_buffer[0]
+        multi_step_experience = Experience(
+            first_experience.state, first_experience.action, reward, done, next_state
+        )
+
+        return multi_step_experience, exp.reward, exp.done
+
+    def n_step(self, device: torch.device) -> Experience:
+        """
+        Takes a  single step in the environment and appends it to the n-step buffer
+
+        Returns:
+            Experience
+        """
+        exp, _, _ = super().step(device)
+        self.n_step_buffer.append(exp)
+        return exp
+
+    def get_transition_info(self) -> Tuple[np.float, np.array, np.int]:
+        """
+        get the accumulated transition info for the n_step_buffer
+        Args:
+            gamma: discount factor
+
+        Returns:
+            multi step reward, final observation and done
+        """
+        last_experience = self.n_step_buffer[-1]
+        final_state = last_experience.new_state
+        done = last_experience.done
+        reward = last_experience.reward
+
+        # calculate reward
+        # in reverse order, go through all the experiences up till the first experience
+        for experience in reversed(list(self.n_step_buffer)[:-1]):
+            reward_t = experience.reward
+            new_state_t = experience.new_state
+            done_t = experience.done
+
+            reward = reward_t + self.gamma * reward * (1 - done_t)
+            final_state, done = (new_state_t, done_t) if done_t else (final_state, done)
+
+        return reward, final_state, done
+
+
+class EpisodicExperienceStream(ExperienceSource, IterableDataset):
+    """
+    Basic experience stream that iteratively yield the current experience of the agent in the env
+
+    Args:
+        env: Environmen that is being used
+        agent: Agent being used to make decisions
+    """
+
+    def __init__(self, env, agent, device: torch.device, episodes: int = 1):
+        super().__init__(env, agent)
+        self.episodes = episodes
+        self.device = device
+
+    def __getitem__(self, item):
+        return item
+
+    def __iter__(self) -> List[Experience]:
+        """
+        Plays a step through the environment until the episode is complete
+
+        Returns:
+            Batch of all transitions for the entire episode
+        """
+        episode_steps, batch = [], []
+
+        while len(batch) < self.episodes:
+            exp = self.step(self.device)
+            episode_steps.append(exp)
+
+            if exp.done:
+                batch.append(episode_steps)
+                episode_steps = []
+
+        yield batch
+
+    def step(self, device: torch.device) -> Experience:
+        """Carries out a single step in the environment"""
+        action = self.agent(self.state, device)
+        new_state, reward, done, _ = self.env.step(action)
+        experience = Experience(
+            state=self.state,
+            action=action,
+            reward=reward,
+            new_state=new_state,
+            done=done,
+        )
+        self.state = new_state
+
+        if done:
+            self.state = self.env.reset()
+
+        return experience

pl_bolts/models/rl/common/experience.py

@@ -1,11 +1,16 @@
 """Experience sources to be used as datasets for Ligthning DataLoaders
 
 Based on implementations found here: https://github.com/Shmuma/ptan/blob/master/ptan/experience.py
+
+..note:: Deprecated, these functions have been moved to pl_bolts.datamodules.experience_source.py
+
 """
+import warnings
 from collections import deque
 from typing import List, Tuple
 
 import numpy as np
+import torch
 from gym import Env
 from torch.utils.data import IterableDataset
 
@@ -24,6 +29,8 @@ class RLDataset(IterableDataset):
     """
 
     def __init__(self, buffer: Buffer, sample_size: int = 1) -> None:
+        warnings.warn("Deprecated, these functions have been moved to pl_bolts.datamodules.experience_source.py",
+                      DeprecationWarning)
         self.buffer = buffer
         self.sample_size = sample_size
 
@@ -74,19 +81,20 @@ class ExperienceSource:
         agent: Agent being used to make decisions
     """
 
-    def __init__(self, env: Env, agent: Agent, device):
+    def __init__(self, env: Env, agent: Agent):
+        warnings.warn("Deprecated, these functions have been moved to pl_bolts.datamodules.experience_source.py",
+                      DeprecationWarning)
         self.env = env
         self.agent = agent
         self.state = self.env.reset()
-        self.device = device
 
     def _reset(self) -> None:
         """resets the env and state"""
         self.state = self.env.reset()
 
-    def step(self) -> Tuple[Experience, float, bool]:
+    def step(self, device: torch.device) -> Tuple[Experience, float, bool]:
         """Takes a single step through the environment"""
-        action = self.agent(self.state, self.device)
+        action = self.agent(self.state, device)
         new_state, reward, done, _ = self.env.step(action)
         experience = Experience(
             state=self.state,
@@ -102,13 +110,13 @@ def step(self) -> Tuple[Experience, float, bool]:
 
         return experience, reward, done
 
-    def run_episode(self) -> float:
+    def run_episode(self, device: torch.device) -> float:
         """Carries out a single episode and returns the total reward. This is used for testing"""
         done = False
         total_reward = 0
 
         while not done:
-            _, reward, done = self.step()
+            _, reward, done = self.step(device)
             total_reward += reward
 
         return total_reward
@@ -117,22 +125,23 @@ def run_episode(self) -> float:
 class NStepExperienceSource(ExperienceSource):
     """Expands upon the basic ExperienceSource by collecting experience across N steps"""
 
-    def __init__(self, env: Env, agent: Agent, device, n_steps: int = 1):
-        super().__init__(env, agent, device)
+    def __init__(self, env: Env, agent: Agent, n_steps: int = 1, gamma: float = 0.99):
+        super().__init__(env, agent)
+        self.gamma = gamma
         self.n_steps = n_steps
         self.n_step_buffer = deque(maxlen=n_steps)
 
-    def step(self) -> Tuple[Experience, float, bool]:
+    def step(self, device: torch.device) -> Tuple[Experience, float, bool]:
         """
         Takes an n-step in the environment
 
         Returns:
             Experience
         """
-        exp = self.single_step()
+        exp = self.single_step(device)
 
         while len(self.n_step_buffer) < self.n_steps:
-            self.single_step()
+            self.single_step(device)
 
         reward, next_state, done = self.get_transition_info()
         first_experience = self.n_step_buffer[0]
@@ -142,18 +151,18 @@ def step(self) -> Tuple[Experience, float, bool]:
 
         return multi_step_experience, exp.reward, exp.done
 
-    def single_step(self) -> Experience:
+    def single_step(self, device: torch.device) -> Experience:
         """
         Takes a  single step in the environment and appends it to the n-step buffer
 
         Returns:
             Experience
         """
-        exp, _, _ = super().step()
+        exp, _, _ = super().step(device)
         self.n_step_buffer.append(exp)
         return exp
 
-    def get_transition_info(self, gamma=0.9) -> Tuple[np.float, np.array, np.int]:
+    def get_transition_info(self) -> Tuple[np.float, np.array, np.int]:
         """
         get the accumulated transition info for the n_step_buffer
         Args:
@@ -174,7 +183,7 @@ def get_transition_info(self, gamma=0.9) -> Tuple[np.float, np.array, np.int]:
             new_state_t = experience.new_state
             done_t = experience.done
 
-            reward = reward_t + gamma * reward * (1 - done_t)
+            reward = reward_t + self.gamma * reward * (1 - done_t)
             final_state, done = (new_state_t, done_t) if done_t else (final_state, done)
 
         return reward, final_state, done
@@ -189,9 +198,10 @@ class EpisodicExperienceStream(ExperienceSource, IterableDataset):
         agent: Agent being used to make decisions
     """
 
-    def __init__(self, env: Env, agent: Agent, device, episodes: int = 1):
-        super().__init__(env, agent, device)
+    def __init__(self, env: Env, agent: Agent, device: torch.device, episodes: int = 1):
+        super().__init__(env, agent)
         self.episodes = episodes
+        self.device = device
 
     def __getitem__(self, item):
         return item
@@ -206,7 +216,7 @@ def __iter__(self) -> List[Experience]:
         episode_steps, batch = [], []
 
         while len(batch) < self.episodes:
-            exp = self.step()
+            exp = self.step(self.device)
             episode_steps.append(exp)
 
             if exp.done:
@@ -215,9 +225,9 @@ def __iter__(self) -> List[Experience]:
 
         yield batch
 
-    def step(self) -> Experience:
+    def step(self, device: torch.device) -> Experience:
         """Carries out a single step in the environment"""
-        action = self.agent(self.state, self.device)
+        action = self.agent(self.state, device)
         new_state, reward, done, _ = self.env.step(action)
         experience = Experience(
             state=self.state,