compatibility with gym > 0.10.5

Author: mlanas

.gitignore

@@ -1,3 +1,5 @@
 __pycache__/
 *.egg-info/
-*.pyc
+*.pyc
+.venv
+.vscode

README.md

@@ -1,5 +1,3 @@
-**Status:** Archive (code is provided as-is, no updates expected)
-
 # Multi-Agent Particle Environment
 
 A simple multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics.
@@ -12,7 +10,7 @@ Used in the paper [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive En
 - To interactively view moving to landmark scenario (see others in ./scenarios/):
 `bin/interactive.py --scenario simple.py`
 
-- Known dependencies: Python (3.5.4), OpenAI gym (0.10.5), numpy (1.14.5)
+- Tested with: Python (3.8.10), OpenAI gym (0.18.3), numpy (1.21)
 
 - To use the environments, look at the code for importing them in `make_env.py`.
 

multiagent/multi_discrete.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 import gym
-from gym.spaces import prng
+from gym.utils import seeding
 
 class MultiDiscrete(gym.Space):
  """
@@ -27,10 +27,12 @@ def __init__(self, array_of_param_array):
  self.high = np.array([x[1] for x in array_of_param_array])
  self.num_discrete_space = self.low.shape[0]
 
+ self.random = seeding.np_random()
+
  def sample(self):
  """ Returns a array with one sample from each discrete action space """
  # For each row: round(random .* (max - min) + min, 0)
- random_array = prng.np_random.rand(self.num_discrete_space)
+ random_array = self.random.rand(self.num_discrete_space)
  return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)]
  def contains(self, x):
  return len(x) == self.num_discrete_space and (np.array(x) >= self.low).all() and (np.array(x) <= self.high).all()

multiagent/rendering.py

@@ -11,18 +11,27 @@
  os.environ['DYLD_FALLBACK_LIBRARY_PATH'] += ':/usr/lib'
  # (JDS 2016/04/15): avoid bug on Anaconda 2.3.0 / Yosemite
 
-from gym.utils import reraise
 from gym import error
 
 try:
  import pyglet
 except ImportError as e:
- reraise(suffix="HINT: you can install pyglet directly via 'pip install pyglet'. But if you really just want to install all Gym dependencies and not have to think about it, 'pip install -e .[all]' or 'pip install gym[all]' will do it.")
+ raise ImportError('''
+ Cannot import pyglet.
+ HINT: you can install pyglet directly via 'pip install pyglet'.
+ But if you really just want to install all Gym dependencies and not have to think about it,
+ 'pip install -e .[all]' or 'pip install gym[all]' will do it.
+ ''')
 
 try:
  from pyglet.gl import *
 except ImportError as e:
- reraise(prefix="Error occured while running `from pyglet.gl import *`",suffix="HINT: make sure you have OpenGL install. On Ubuntu, you can run 'apt-get install python-opengl'. If you're running on a server, you may need a virtual frame buffer; something like this should work: 'xvfb-run -s \"-screen 0 1400x900x24\" python <your_script.py>'")
+ raise ImportError('''
+ Error occured while running `from pyglet.gl import *`
+ HINT: make sure you have OpenGL install. On Ubuntu, you can run 'apt-get install python-opengl'.
+ If you're running on a server, you may need a virtual frame buffer; something like this should work:
+ 'xvfb-run -s \"-screen 0 1400x900x24\" python <your_script.py>'
+ ''')
 
 import math
 import numpy as np

multiagent/scenario.py

@@ -8,3 +8,9 @@ def make_world(self):
  # create initial conditions of the world
  def reset_world(self, world):
  raise NotImplementedError()
+ # return agents rewards
+ def reward(self, agent, world):
+ raise NotImplementedError()
+ # return agents observations
+ def observation(self, agent, world):
+ raise NotImplementedError()