I need to find a method to get all possible next states for every state in OpenAI Gym environments. My goal is to build a graph where each state connects to all states it can transition to through any action.
Is there a built-in way to do this? I want to avoid running the environment randomly until I discover all transitions since that could take way too long.
I heard MuJoCo has some state setting functions but I prefer to work with standard Gym environments. Can I somehow force an environment to jump to a specific state and then check what states are reachable from there?
I’m working on topological value iteration which needs this complete state transition graph. I’m new to OpenAI Gym so detailed explanations would help a lot.
Here’s what I’m trying to avoid:
# This brute force approach is too slow
env = gym.make('FrozenLake-v1')
discovered_transitions = {}
for episode in range(1000):
current_state = env.reset()
done = False
while not done:
action = env.action_space.sample()
next_state, reward, done, info = env.step(action)
if current_state not in discovered_transitions:
discovered_transitions[current_state] = set()
discovered_transitions[current_state].add(next_state)
current_state = next_state
Is there a more direct approach to extract the complete transition structure?
You can actually access the transition model directly for most discrete environments - no need to run episodes. Tabular MDPs in Gym store their transition probabilities as matrices or dictionaries internally.
For FrozenLake, just grab the P attribute which has the complete transition structure:
env = gym.make('FrozenLake-v1')
transition_model = env.unwrapped.P
# P[state][action] gives you list of (probability, next_state, reward, done)
for state in range(env.observation_space.n):
for action in range(env.action_space.n):
transitions = transition_model[state][action]
for prob, next_state, reward, done in transitions:
print(f"State {state} -> Action {action} -> State {next_state} (prob: {prob})")
This works for FrozenLake, Taxi, CliffWalking and other discrete tabular environments. The unwrapped attribute gets you into the underlying environment where these transition matrices live.
Continuous state spaces or complex environments? You’re stuck with sampling or hunting down environment-specific APIs. But for standard discrete MDPs, this direct method beats your brute force approach by miles.
Try using env.env or env.unwrapped to access internal state manipulation methods. Most gym environments have hidden setter methods that aren’t in the main API docs. I’ve done this with CartPole and MountainCar - just dig into their source code to find the setter methods. Some have set_state() or _set_state() methods that let you manually position the environment anywhere you want. Once you can set arbitrary states, just iterate through the state space, set each state, try all actions, and record the transitions. You get deterministic coverage without random sampling. Check the environment’s source for methods that restore or initialize state variables. They’re sometimes called reset_to_state() or hidden in the initialization code. This worked great for my policy iteration project where I needed complete transition graphs for planning algorithms.