Implemented methods to save and restore PyBullet states.

docs/conf.py

@@ -22,7 +22,7 @@
 author = 'Quentin Gallouédec'
 
 # The full version, including alpha/beta/rc tags
-release = 'v2.0.3'
+release = 'v2.0.4'
 
 
 # -- General configuration ---------------------------------------------------

docs/index.rst

@@ -19,6 +19,7 @@ Welcome to panda-gym's documentation!
 
    usage/environments
    usage/manual_control
+   usage/save_restore_state
    usage/train_with_sb3
 
 .. toctree::

docs/usage/save_restore_state.rst

@@ -0,0 +1,31 @@
+.. _save_restore_states:
+
+Save and Restore States
+==============
+
+It is possible to save a state of the entire simulation environment. This is useful if your application requires lookahead search. Below is an example of a greedy random search.
+
+.. code-block:: python
+
+    import gym
+    import panda_gym
+
+    env = gym.make("PandaReachDense-v2", render=True)
+    obs = env.reset()
+
+    while True:
+        state_id = env.save_state()
+        best_action = None
+        rew = best_rew = env.task.compute_reward(
+            obs["achieved_goal"], obs["desired_goal"], None) 
+
+        while rew <= best_rew:
+            env.restore_state(state_id)
+            a = env.action_space.sample()
+            _, rew, _, _ = env.step(a)
+
+        env.restore_state(state_id)
+        obs, _, _, _ = env.step(a)
+        env.remove_state(state_id)
+
+    env.close()

panda_gym/envs/core.py

@@ -227,6 +227,7 @@ def __init__(self, robot: PyBulletRobot, task: Task) -> None:
         )
         self.action_space = self.robot.action_space
         self.compute_reward = self.task.compute_reward
+        self._saved_goal = dict()
 
     def _get_obs(self) -> Dict[str, np.ndarray]:
         robot_obs = self.robot.get_obs()  # robot state
@@ -246,6 +247,19 @@ def reset(self, seed: Optional[int] = None) -> Dict[str, np.ndarray]:
             self.task.reset()
         return self._get_obs()
 
+    def save_state(self) -> int:
+        state_id = self.sim.save_state()
+        self._saved_goal[state_id] = self.task.goal
+        return state_id
+
+    def restore_state(self, state_id: int) -> None:
+        self.sim.restore_state(state_id)
+        self.task.goal = self._saved_goal[state_id]
+
+    def remove_state(self, state_id: int) -> None:
+        self._saved_goal.pop(state_id)
+        self.sim.remove_state(state_id)
+
     def step(self, action: np.ndarray) -> Tuple[Dict[str, np.ndarray], float, bool, Dict[str, Any]]:
         self.robot.set_action(action)
         self.sim.step()

panda_gym/pybullet.py

@@ -57,6 +57,31 @@ def close(self) -> None:
         """Close the simulation."""
         self.physics_client.disconnect()
 
+    def save_state(self) -> int:
+        """Save the current simulation state.
+
+        Returns:
+            int: A state id assigned by PyBullet, which is the first non-negative
+            integer available for indexing.
+        """
+        return self.physics_client.saveState()
+
+    def restore_state(self, state_id: int) -> None:
+        """Restore a simulation state.
+
+        Args:
+            state_id: The simulation state id returned by save_state().
+        """
+        self.physics_client.restoreState(state_id)
+
+    def remove_state(self, state_id: int) -> None:
+        """Remove a simulation state. This will make this state_id available again for returning in save_state().
+
+        Args:
+            state_id: The simulation state id returned by save_state().
+        """
+        self.physics_client.removeState(state_id)
+
     def render(
         self,
         mode: str = "human",

panda_gym/version.txt

@@ -1 +1 @@
-2.0.3
+2.0.4

test/save_and_restore_test.py

@@ -0,0 +1,36 @@
+import gym
+import numpy as np
+import pybullet
+import pytest
+
+import panda_gym
+
+
+def test_save_and_restore_state():
+    env = gym.make("PandaReach-v2")
+    env.reset()
+
+    state_id = env.save_state()
+
+    # Perform the action
+    action = env.action_space.sample()
+    next_obs1, reward, done, info = env.step(action)
+
+    # Restore and perform the same action
+    env.reset()
+    env.restore_state(state_id)
+    next_obs2, reward, done, info = env.step(action)
+
+    # The observations in both cases should be equals
+    assert np.all(next_obs1["achieved_goal"] == next_obs2["achieved_goal"])
+    assert np.all(next_obs1["observation"] == next_obs2["observation"])
+    assert np.all(next_obs1["desired_goal"] == next_obs2["desired_goal"])
+
+
+def test_remove_state():
+    env = gym.make("PandaReach-v2")
+    env.reset()
+    state_id = env.save_state()
+    env.remove_state(state_id)
+    with pytest.raises(pybullet.error):
+        env.restore_state(state_id)