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.

import gymnasium as gym
import numpy as np

import panda_gym

env = gym.make("PandaReachDense-v3", render_mode="human")
observation, _ = env.reset()

for _ in range(1000):
    state_id = env.save_state()

    # Sample 5 actions and choose the one that yields the best reward.
    best_reward = -np.inf
    best_action = None
    for _ in range(5):
        action = env.action_space.sample()
        observation, reward, _, _, _ = env.step(action)
        if reward > best_reward:
            best_reward = reward
            best_action = action

    env.remove_state(state_id)  # discard the state, as it is no longer needed

    # Step with the best action
    observation, reward, terminated, truncated, info = env.step(best_action)

    if terminated:
        observation, info = env.reset()