I’m working with OpenAI Gym and need to find environments that allow me to specify where the agent begins. I’ve been using MountainCarContinuous-v0, which lets me choose the car’s starting position, but I need something more challenging.
Does anyone know other gym environments that support custom initial states? I’m particularly interested in more complex scenarios than the mountain car problem. Any suggestions would be really helpful!
Here’s what I’m trying to achieve:
import gym
def setup_environment():
env = gym.make('SomeEnvironment-v0')
custom_start = [0.2, 0.5, 0.1] # example initial values
env.reset()
env.set_state(custom_start) # this is what I'm looking for
return env
my_env = setup_environment()
observation = my_env.step(action)[0]
I want to train my agent from specific starting points rather than random initialization every time.
Custom initial states are tricky since most gym environments don’t have a standard way to do this. I’ve had good luck with Atari games like Breakout-v4 - you can mess with the RAM directly using env.unwrapped.ale.restoreState() after saving a state with env.unwrapped.ale.cloneState(). Gives you exact control over where the game starts. For continuous control stuff, skip standard gym and go with PyBullet environments instead. They’re way better for state manipulation through their physics engines. I also built a wrapper that stores several reset states and picks randomly from them - gives you controlled variety without full customization headaches. Bottom line: figure out what engine your environment uses and access the unwrapped version.
try lunarlander-v2 - you can set custom starting positions using env.unwrapped.lander attributes after reset. bipedalwalker also works if you tweak the hull position through the box2d world directly. takes some digging but it’s solid for testing specific scenarios.
Been dealing with this exact problem for years. Manual state manipulation gets messy fast, especially when running experiments across different environments.
Now I automate the whole workflow. I define multiple starting conditions in a config file, then automatically cycle through them during training. This lets me test hundreds of different starting positions without touching code.
The real game changer is automating environment setup itself. I create workflows that spawn different gym environments, apply custom states, run training episodes, and collect data automatically. No more manual tweaking or writing custom wrappers for each environment.
You can set up triggers that automatically switch between environments based on performance metrics. Start with simple ones like CartPole with custom initial angles, then automatically progress to complex scenarios like manipulator arms or multi-agent setups.
I handle everything from environment creation to results analysis without manual intervention. The automation handles all state management complexity while I focus on actual RL algorithm improvements.
This approach works with any gym environment since you’re orchestrating from outside rather than hacking internal APIs. Much cleaner and scales way better when testing dozens of scenarios.
I encountered similar challenges while working on my reinforcement learning projects. Although many Gym environments lack a direct set_state() function, you can typically employ methods within their physics engines to achieve your goals. For example, in CartPole-v1, you can manipulate the state directly by setting env.state to your desired custom parameters after calling the reset() method. For more complex environments such as HalfCheetah-v2 and Ant-v2, accessing env.sim.set_state() can be beneficial, assuming you have the appropriate license. Additionally, I found wrapping environments in a custom class and modifying the reset logic to set initial conditions very effective, especially with Pendulum-v0 and Acrobot-v1, where internal state variables are accessible post-initialization. This approach can enhance flexibility significantly.