Merge pull request #54 from NREL/ray_v2

Update to use ray v2.3

docs/source/background/introduction.rst

@@ -75,5 +75,6 @@ random walk down a 1D corridor:
 
    while not done:
       action = random.choice(range(len(env.state.children)))
-      obs, reward, done, info = env.step(action)
+      obs, reward, terminated, truncated, info = env.step(action)
+      done = terminated or truncated
       total_reward += reward

docs/source/examples/hallway.ipynb

@@ -334,8 +334,9 @@
     }
    ],
    "source": [
-    "obs = env.reset()\n",
-    "print(obs)"
+    "obs, info = env.reset()\n",
+    "print(obs)\n",
+    "print(info)"
    ]
   },
   {
@@ -379,7 +380,7 @@
    ],
    "source": [
     "# Not a valid action\n",
-    "obs, rew, done, info = env.step(1)"
+    "obs, rew, terminated, truncated, info = env.step(1)"
    ]
   },
   {
@@ -390,7 +391,7 @@
    "outputs": [],
    "source": [
     "# A valid action\n",
-    "obs, rew, done, info = env.step(0)"
+    "obs, rew, terminated, truncated, info = env.step(0)"
    ]
   },
   {
@@ -504,7 +505,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obs, rew, done, info = env.step(1)"
+    "obs, rew, terminated, truncated, info = env.step(1)"
    ]
   },
   {
@@ -604,7 +605,7 @@
     "env.step(0)\n",
     "\n",
     "for _ in range(5):\n",
-    "    obs, rew, done, info = env.step(1)\n",
+    "    obs, rew, terminated, truncated, info = env.step(1)\n",
     "\n",
     "env.make_observation()"
    ]

docs/source/examples/tsp_docs.ipynb

@@ -381,7 +381,8 @@
     "rand_rew = 0.\n",
     "while not done:\n",
     "    action = env.action_space.sample()\n",
-    "    _, rew, done, _ = env.step(action)\n",
+    "    _, rew, terminated, truncated, _ = env.step(action)\n",
+    "    done = terminated or truncated\n",
     "    rand_rew += rew\n",
     "    \n",
     "print(f\"Random reward = {rand_rew}\")\n",
@@ -425,15 +426,16 @@
     }
    ],
    "source": [
-    "obs = env.reset()\n",
+    "obs, info = env.reset()\n",
     "\n",
     "done = False\n",
     "greedy_rew = 0.\n",
     "i = 0\n",
     "while not done:\n",
     "    # Get the node with shortest distance to the parent (current) node\n",
     "    idx = np.argmin([x[\"parent_dist\"] for x in obs[1:]])    \n",
-    "    obs, rew, done, _ = env.step(idx)\n",
+    "    obs, rew, terminated, truncated, _ = env.step(idx)\n",
+    "    done = terminated or truncated\n",
     "    greedy_rew += rew\n",
     "    \n",
     "print(f\"Greedy reward = {greedy_rew}\")\n",

docs/source/examples/tsp_env.ipynb

@@ -62,7 +62,7 @@
     "%%capture\n",
     "\n",
     "# Reset the environment and initialize the observation, reward, and done fields\n",
-    "obs = env.reset()\n",
+    "obs, info = env.reset()\n",
     "greedy_reward = 0\n",
     "done = False\n",
     "\n",
@@ -74,7 +74,8 @@
     "\n",
     "        # Get the observation for the next set of candidate nodes,\n",
     "        # incremental reward, and done flags\n",
-    "        obs, reward, done, info = env.step(action)\n",
+    "        obs, reward, terminated, truncated, info = env.step(action)\n",
+    "        done = terminated or truncated\n",
     "\n",
     "        # Append the step's reward to the running total\n",
     "        greedy_reward += reward\n",
@@ -182,7 +183,7 @@
     "    )[:k]\n",
     "\n",
     "    for entry in top_actions:\n",
-    "        obs, reward, done, info = entry[\"env\"].step(entry[\"action_index\"])\n",
+    "        obs, reward, terminated, truncated, info = entry[\"env\"].step(entry[\"action_index\"])\n",
     "\n",
     "    return [(entry[\"env\"], entry[\"reward\"]) for entry in top_actions], done"
    ]
@@ -194,7 +195,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obs = env.reset()\n",
+    "obs, info = env.reset()\n",
     "env_list = [(env, 0)]\n",
     "done = False\n",
     "\n",
@@ -212,7 +213,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obs = env.reset()\n",
+    "obs, info = env.reset()\n",
     "env_list = [(env, 0)]\n",
     "done = False\n",
     "\n",

experiments/hallway/custom_env.py

@@ -17,7 +17,7 @@
 import os
 import random
 
-import gym
+import gymnasium as gym
 import ray
 from gym.spaces import Box, Discrete
 from ray import tune
@@ -87,19 +87,27 @@ def __init__(self, config: EnvContext):
         # Set the seed. This is only used for the final (reach goal) reward.
         self.seed(config.worker_index * config.num_workers)
 
-    def reset(self):
+    def reset(self, *, seed=None, options=None):
         self.cur_pos = 0
-        return [self.cur_pos]
+        info_dict = {}
+        return [self.cur_pos], info_dict
 
     def step(self, action):
         assert action in [0, 1], action
         if action == 0 and self.cur_pos > 0:
             self.cur_pos -= 1
         elif action == 1:
             self.cur_pos += 1
-        done = self.cur_pos >= self.end_pos
+        terminated = self.cur_pos >= self.end_pos
+        truncated = False
         # Produce a random reward when we reach the goal.
-        return [self.cur_pos], random.random() * 2 if done else -0.1, done, {}
+        return (
+            [self.cur_pos], 
+            random.random() * 2 if terminated else -0.1, 
+            terminated, 
+            truncated, 
+            {}
+        )
 
     def seed(self, seed=None):
         random.seed(seed)

experiments/tsp/untrained_model_sampling.ipynb

@@ -137,7 +137,8 @@
     "        )\n",
     "        action_probabilities = tf.nn.softmax(masked_action_values).numpy()\n",
     "        action = np.random.choice(env.max_num_children, size=1, p=action_probabilities)[0]\n",
-    "        obs, reward, done, info = env.step(action)\n",
+    "        obs, reward, terminated, truncated, info = env.step(action)\n",
+    "        done = terminated or truncated\n",
     "        total_reward += reward\n",
     "        \n",
     "    return total_reward"
@@ -213,7 +214,7 @@
     }
    ],
    "source": [
-    "obs = env.reset()\n",
+    "obs, info = env.reset()\n",
     "env.observation_space.contains(obs)"
    ]
   },
@@ -376,11 +377,12 @@
     "    # run until episode ends\n",
     "    episode_reward = 0\n",
     "    done = False\n",
-    "    obs = env.reset()\n",
+    "    obs, info = env.reset()\n",
     "\n",
     "    while not done:\n",
     "        action = agent.compute_single_action(obs)\n",
-    "        obs, reward, done, info = env.step(action)\n",
+    "        obs, reward, terminated, truncated, info = env.step(action)\n",
+    "        done = terminated or truncated\n",
     "        episode_reward += reward\n",
     "        \n",
     "    return episode_reward"

graphenv/examples/hallway/hallway_state.py

@@ -1,7 +1,7 @@
 import random
 from typing import Dict, Sequence
 
-import gym
+import gymnasium as gym
 import numpy as np
 from graphenv import tf
 from graphenv.vertex import Vertex

graphenv/examples/tsp/tsp_nfp_state.py

@@ -1,7 +1,7 @@
 from math import sqrt
 from typing import Dict, Optional
 
-import gym
+import gymnasium as gym
 import numpy as np
 from graphenv.examples.tsp.tsp_preprocessor import TSPPreprocessor
 from graphenv.examples.tsp.tsp_state import TSPState

graphenv/examples/tsp/tsp_state.py

@@ -1,6 +1,6 @@
 from typing import Any, Callable, Dict, List, Optional, Sequence
 
-import gym
+import gymnasium as gym
 import networkx as nx
 import numpy as np
 from graphenv import tf

graphenv/graph_env.py

@@ -2,7 +2,7 @@
 import warnings
 from typing import Any, Dict, List, Optional, Tuple
 
-import gym
+import gymnasium as gym
 import numpy as np
 from ray.rllib.env.env_context import EnvContext
 from ray.rllib.utils.spaces.repeated import Repeated
@@ -61,17 +61,17 @@ def __init__(self, env_config: EnvContext) -> None:
         self.action_space = gym.spaces.Discrete(self.max_num_children)
         logger.debug("leaving graphenv construction")
 
-    def reset(self) -> Dict[str, np.ndarray]:
+    def reset(self, *, seed=None, options=None) -> Tuple[Dict[str, np.ndarray], Dict]:
         """Reset this state to the root vertex. It is possible for state.root to
         return different root vertices on each call.
 
         Returns:
             Dict[str, np.ndarray]: Observation of the root vertex.
         """
         self.state = self.state.root
-        return self.make_observation()
+        return self.make_observation(), self.state.info
 
-    def step(self, action: int) -> Tuple[Dict[str, np.ndarray], float, bool, dict]:
+    def step(self, action: int) -> Tuple[Dict[str, np.ndarray], float, bool, bool, dict]:
         """Steps the environment to a new state by taking an action. In the
         case of GraphEnv, the action specifies which next vertex to move to and
         this method advances the environment to that vertex.
@@ -86,7 +86,8 @@ def step(self, action: int) -> Tuple[Dict[str, np.ndarray], float, bool, dict]:
             Tuple[Dict[str, np.ndarray], float, bool, dict]: Tuple of:
                 a dictionary of the new state's observation,
                 the reward received by moving to the new state's vertex,
-                a bool which is true iff the new stae is a terminal vertex,
+                a bool which is true iff the new state is a terminal vertex,
+                a bool which is true if the search is truncated
                 a dictionary of debugging information related to this call
         """
 
@@ -115,10 +116,17 @@ def step(self, action: int) -> Tuple[Dict[str, np.ndarray], float, bool, dict]:
                 RuntimeWarning,
             )
 
+        # In RLlib 2.3, the config options "no_done_at_end", "horizon", and "soft_horizon" are no longer supported
+        # according to the migration guide https://docs.google.com/document/d/1lxYK1dI5s0Wo_jmB6V6XiP-_aEBsXDykXkD1AXRase4/edit#
+        # Instead, wrap your gymnasium environment with a TimeLimit wrapper, 
+        # which will set truncated according to the number of timesteps 
+        # see https://gymnasium.farama.org/api/wrappers/misc_wrappers/#gymnasium.wrappers.TimeLimit
+        truncated = False
         result = (
             self.make_observation(),
             self.state.reward,
             self.state.terminal,
+            truncated,
             self.state.info,
         )
         logger.debug(

graphenv/graph_model.py

@@ -2,7 +2,7 @@
 from abc import abstractmethod
 from typing import Dict, List, Tuple
 
-import gym
+import gymnasium as gym
 from ray.rllib.models.repeated_values import RepeatedValues
 from ray.rllib.utils.typing import TensorStructType, TensorType
 

graphenv/vertex.py

@@ -1,7 +1,7 @@
 from abc import abstractmethod
 from typing import Any, Dict, Generic, List, Optional, Sequence, TypeVar
 
-import gym
+import gymnasium as gym
 
 V = TypeVar("V")
 

setup.cfg

@@ -12,9 +12,10 @@ classifiers =
 packages = find:
 install_requires =
   networkx==3.0
-  ray[tune,rllib]==2.2.0
+  ray[tune,rllib]==2.3.1
   numpy<1.24.0
   tqdm==4.64.1
+  matplotlib
 
 [options.extras_require]
 tensorflow = tensorflow

tests/test_hallway.py

@@ -61,14 +61,14 @@ def test_graphenv_reset(hallway_env: GraphEnv):
 
 
 def test_graphenv_step(hallway_env: GraphEnv):
-    obs, reward, terminal, info = hallway_env.step(0)
+    obs, reward, terminal, truncated, info = hallway_env.step(0)
 
     for _ in range(3):
         assert terminal is False
         assert reward == -0.1
         assert hallway_env.observation_space.contains(obs)
         assert hallway_env.action_space.contains(1)
-        obs, reward, terminal, info = hallway_env.step(1)
+        obs, reward, terminal, truncated, info = hallway_env.step(1)
 
     assert terminal is True
     assert reward > 0

tests/test_tsp.py

@@ -27,7 +27,7 @@ def test_graphenv():
         }
     )
 
-    obs = env.reset()
+    obs, info = env.reset()
     assert env.observation_space.contains(obs)