Make new gymnasium-based 2.2 candidate working with Ray RLlib

diambra/arena/ray_rllib/make_ray_env.py

@@ -1,15 +1,13 @@
 import os
 import diambra.arena
 import logging
-import gym
+import gymnasium as gym
 from ray.rllib.env.env_context import EnvContext
 from copy import deepcopy
 import pickle
 
 class DiambraArena(gym.Env):
-
  def __init__(self, config: EnvContext):
-
  self.logger = logging.getLogger(__name__)
 
  # If to load environment spaces from a file
@@ -31,7 +29,6 @@ def __init__(self, config: EnvContext):
  self.env_spaces_file_name = config["env_spaces_file_name"]
 
  if self.load_spaces_from_file is False:
-
  if "is_rollout" not in config.keys():
  message = "Environment initialized without a preprocessed config file."
  message += " Make sure to call \"preprocess_ray_config\" before initializing Ray RL Algorithms."
@@ -40,7 +37,7 @@ def __init__(self, config: EnvContext):
  self.game_id = config["game_id"]
  self.settings = config["settings"] if "settings" in config.keys() else {}
  self.wrappers_settings = config["wrappers_settings"] if "wrappers_settings" in config.keys() else {}
- self.seed = config["seed"] if "seed" in config.keys() else 0
+ self.render_mode = config["render_mode"] if "render_mode" in config.keys() else None
 
  num_rollout_workers = config["num_workers"]
  num_eval_workers = config["evaluation_num_workers"]
@@ -68,8 +65,7 @@ def __init__(self, config: EnvContext):
 
  self.logger.debug("Rank: {}".format(self.rank))
 
- self.env = diambra.arena.make(self.game_id, self.settings, self.wrappers_settings,
- seed=self.seed + self.rank, rank=self.rank)
+ self.env = diambra.arena.make(self.game_id, self.settings, self.wrappers_settings, render_mode=self.render_mode, rank=self.rank)
 
  env_spaces_dict = {}
  env_spaces_dict["action_space"] = self.env.action_space
@@ -93,8 +89,11 @@ def __init__(self, config: EnvContext):
  self.action_space = env_spaces_dict["action_space"]
  self.observation_space = env_spaces_dict["observation_space"]
 
- def reset(self):
- return self.env.reset()
+ def reset(self, seed=None, options=None):
+ if self.load_spaces_from_file is True:
+ return self.observation_space.sample(), {}
+ else:
+ return self.env.reset(seed=seed, options=options)
 
  def step(self, action):
  return self.env.step(action)
@@ -103,7 +102,6 @@ def render(self):
  return self.env.render()
 
 def preprocess_ray_config(config):
-
  logger = logging.getLogger(__name__)
 
  num_envs_required = 0

setup.py

@@ -14,7 +14,7 @@
  'tests': ['pytest', 'pytest-mock', 'testresources'],
  'stable-baselines': ['stable-baselines==2.10.2', 'gym<=0.21.0', "protobuf==3.20.1", "pyyaml"],
  'stable-baselines3': ['stable-baselines3[extra]==2.1.0', "pyyaml"],
- 'ray-rllib': ['ray[rllib]==2.0.0', 'tensorflow<=2.10.0', 'torch<=1.12.1', "pyyaml"],
+ 'ray-rllib': ['ray[rllib]==2.6.3', 'tensorflow', 'torch', "pyyaml"],
 }
 
 # NOTE Package data is inside MANIFEST.In