initial commit (#2170)

pseudo-rnd-thoughts · web-flow · commit b24c7c346587 · 2023-01-23T20:43:50.000Z
diff --git a/intermediate_source/reinforcement_q_learning.py b/intermediate_source/reinforcement_q_learning.py
@@ -3,17 +3,18 @@
 Reinforcement Learning (DQN) Tutorial
 =====================================
 **Author**: `Adam Paszke <https://github.com/apaszke>`_
+            `Mark Towers <https://github.com/pseudo-rnd-thoughts>`_
 
 
 This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent
-on the CartPole-v1 task from the `OpenAI Gym <https://www.gymlibrary.dev/>`__.
+on the CartPole-v1 task from `Gymnasium <https://www.gymnasium.farama.org>`__.
 
 **Task**
 
 The agent has to decide between two actions - moving the cart left or
-right - so that the pole attached to it stays upright. You can find an
-official leaderboard with various algorithms and visualizations at the
-`Gym website <https://www.gymlibrary.dev/environments/classic_control/cart_pole>`__.
+right - so that the pole attached to it stays upright. You can find more
+information about the environment and other more challenging environments at
+`Gymnasium's website <https://gymnasium.farama.org/environments/classic_control/cart_pole/>`__.
 
 .. figure:: /_static/img/cartpole.gif
    :alt: cartpole
@@ -24,7 +25,7 @@
 an action, the environment *transitions* to a new state, and also
 returns a reward that indicates the consequences of the action. In this
 task, rewards are +1 for every incremental timestep and the environment
-terminates if the pole falls over too far or the cart moves more then 2.4
+terminates if the pole falls over too far or the cart moves more than 2.4
 units away from center. This means better performing scenarios will run
 for longer duration, accumulating larger return.
 
@@ -41,13 +42,15 @@
 
 
 First, let's import needed packages. Firstly, we need
-`gym <https://github.com/openai/gym>`__ for the environment
-Install by using `pip`. If you are running this in Google colab, run:
+`gymnasium <https://gymnasium.farama.org/>`__ for the environment,
+installed by using `pip`. This is a fork of the original OpenAI
+Gym project and maintained by the same team since Gym v0.19.
+If you are running this in Google colab, run:
 
 .. code-block:: bash
 
    %%bash
-   pip3 install gym[classic_control]
+   pip3 install gymnasium[classic_control]
 
 We'll also use the following from PyTorch:
 
@@ -57,10 +60,9 @@
 
 """
 
-import gym
+import gymnasium as gym
 import math
 import random
-import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
 from collections import namedtuple, deque
@@ -71,12 +73,7 @@
 import torch.optim as optim
 import torch.nn.functional as F
 
-if gym.__version__[:4] == '0.26':
-    env = gym.make('CartPole-v1')
-elif gym.__version__[:4] == '0.25':
-    env = gym.make('CartPole-v1', new_step_api=True)
-else:
-    raise ImportError(f"Requires gym v25 or v26, actual version: {gym.__version__}")
+env = gym.make("CartPole-v1")
 
 # set up matplotlib
 is_ipython = 'inline' in matplotlib.get_backend()
@@ -117,7 +114,7 @@
 class ReplayMemory(object):
 
     def __init__(self, capacity):
-        self.memory = deque([],maxlen=capacity)
+        self.memory = deque([], maxlen=capacity)
 
     def push(self, *args):
         """Save a transition"""
@@ -261,10 +258,7 @@ def forward(self, x):
 # Get number of actions from gym action space
 n_actions = env.action_space.n
 # Get the number of state observations
-if gym.__version__[:4] == '0.26':
-    state, _ = env.reset()
-elif gym.__version__[:4] == '0.25':
-    state, _ = env.reset(return_info=True)
+state, info = env.reset()
 n_observations = len(state)
 
 policy_net = DQN(n_observations, n_actions).to(device)
@@ -286,7 +280,7 @@ def select_action(state):
     steps_done += 1
     if sample > eps_threshold:
         with torch.no_grad():
-            # t.max(1) will return largest column value of each row.
+            # t.max(1) will return the largest column value of each row.
             # second column on max result is index of where max element was
             # found, so we pick action with the larger expected reward.
             return policy_net(state).max(1)[1].view(1, 1)
@@ -410,10 +404,7 @@ def optimize_model():
 
 for i_episode in range(num_episodes):
     # Initialize the environment and get it's state
-    if gym.__version__[:4] == '0.26':
-        state, _ = env.reset()
-    elif gym.__version__[:4] == '0.25':
-        state, _ = env.reset(return_info=True)
+    state, info = env.reset()
     state = torch.tensor(state, dtype=torch.float32, device=device).unsqueeze(0)
     for t in count():
         action = select_action(state)
diff --git a/requirements.txt b/requirements.txt
@@ -52,6 +52,7 @@ pillow==9.3.0
 wget
 gym==0.25.1
 gym-super-mario-bros==7.4.0
+gymnasium==0.27.0
 timm
 iopath
 pygame==2.1.2
