How severe does this issue affect your experience of using Ray?
In the MLagent Dodgeball game, there are both discrete and continuous action branches. I read that RLlib does not support mixed action spaces. Can I confirm that try to define action space like below will be impossible? Thanks!
Hi @Jay,
It will depend on the algorithm you want to use. Mainly if that algorithm can support continuous action spaces. If it can the yes they should be supported. Here is an example from the repository:
Thanks for the reply!
Is it possible for you to explain the code to me?
env_config={
"space": Dict(
{
"a": Tuple(
[Dict({"d": Box(-10.0, 10.0, ()), "e": Discrete(2)})]
),
"b": Box(-10.0, 10.0, (2,)),
"c": Discrete(4),
}
),
}
In this code, this is how the space is defined. Is this referring to the action space or the observation space? Is there a meaning to “a”, “b” or “c”?
Thank you for your time!
Hi @Jay,
In this particular environment the observation and action spaces are identical. That does not need to be the case it just happens to be for this example.
There is nothing special about the key names, they can be whatever the environment chooses.
Hi @mannyv
Can I trouble you to look at my implementation?
I am having issues and have this error when running the unity 3d sim (Error: ‘tuple’ object has no attribute 'dtype’).
Thank you.
I am not really set up to run this. Can you share the full error output you get? Which version of ray are you running?
@Jay , hey I am also not setup for a unity-3D env run. However, I took your code and modified it to accept a custom fake env that has your defined spaces, and it runs fine. No complaining about the spaces or anything. I hope it helps.
"""
Example of running an RLlib Trainer against a locally running Unity3D editor
instance (available as Unity3DEnv inside RLlib).
For a distributed cloud setup example with Unity,
see `examples/serving/unity3d_[server|client].py`
To run this script against a local Unity3D engine:
1) Install Unity3D and `pip install mlagents`.
2) Open the Unity3D Editor and load an example scene from the following
ml-agents pip package location:
`.../ml-agents/Project/Assets/ML-Agents/Examples/`
This script supports the `3DBall`, `3DBallHard`, `SoccerStrikersVsGoalie`,
`Tennis`, and `Walker` examples.
Specify the game you chose on your command line via e.g. `--env 3DBall`.
Feel free to add more supported examples here.
3) Then run this script (you will have to press Play in your Unity editor
at some point to start the game and the learning process):
$ python unity3d_env_local.py --env 3DBall --stop-reward [..]
[--framework=torch]?
"""
import argparse
import os
import numpy as np
import ray
from ray import air, tune
from ray.rllib.algorithms.ppo import PPOConfig
from ray.rllib.env.wrappers.unity3d_env import Unity3DEnv
from ray.rllib.utils.test_utils import check_learning_achieved
from ray.rllib.policy.policy import PolicySpec
from gymnasium.spaces import Box, MultiDiscrete, Tuple as TupleSpace
import gymnasium as gym
ray.shutdown()
parser = argparse.ArgumentParser()
parser.add_argument(
"--file-name",
type=str,
default=None,
help="The Unity3d binary (compiled) game, e.g. "
"'/home/ubuntu/soccer_strikers_vs_goalie_linux.x86_64'. Use `None` for "
"a currently running Unity3D editor.",
)
parser.add_argument(
"--from-checkpoint",
type=str,
default=None,
help="Full path to a checkpoint file for restoring a previously saved "
"Trainer state.",
)
parser.add_argument("--num-workers", type=int, default=0)
parser.add_argument(
"--as-test",
action="store_true",
help="Whether this script should be run as a test: --stop-reward must "
"be achieved within --stop-timesteps AND --stop-iters.",
)
parser.add_argument(
"--stop-iters", type=int, default=9999, help="Number of iterations to train."
)
parser.add_argument(
"--stop-timesteps", type=int, default=10000000, help="Number of timesteps to train."
)
parser.add_argument(
"--stop-reward",
type=float,
default=9999.0,
help="Reward at which we stop training.",
)
parser.add_argument(
"--horizon",
type=int,
default=3000,
help="The max. number of `step()`s for any episode (per agent) before "
"it'll be reset again automatically.",
)
parser.add_argument(
"--framework",
choices=["tf", "tf2", "torch"],
default="tf",
help="The DL framework specifier.",
)
class MyCustomEnvWithNestedSpaces(gym.Env):
def __init__(self, config):
self.observation_space = TupleSpace(
[
Box(float("-inf"), float("inf"), (3,8)),
Box(float("-inf"), float("inf"), (738,)),
Box(float("-inf"), float("inf"), (252,)),
Box(float("-inf"), float("inf"), (36,)),
Box(float("-inf"), float("inf"), (378,)),
Box(float("-inf"), float("inf"), (20,))
]
)
self.action_space = TupleSpace(
[
Box(-1.0, 1.0, (3,), dtype = np.float32),
MultiDiscrete([2,2])
]
)
def reset(self, *, seed=None, options=None):
self.steps = 0
return self._next_obs(), {}
def step(self, action):
self.steps += 1
reward = 0.0
return self._next_obs(), reward, self.steps >= 50, False, {}
def _next_obs(self):
self.current_obs = self.observation_space.sample()
return self.current_obs
if __name__ == "__main__":
ray.init()
args = parser.parse_args()
tune.register_env("custom", lambda c: MyCustomEnvWithNestedSpaces(c))
# # Get policies (different agent types; "behaviors" in MLAgents) and
# # the mappings from individual agents to Policies.
# policies = {
# "DodgeballAgent": PolicySpec(
# observation_space=TupleSpace(
# [
# Box(float("-inf"), float("inf"), (3,8)),
# Box(float("-inf"), float("inf"), (738,)),
# Box(float("-inf"), float("inf"), (252,)),
# Box(float("-inf"), float("inf"), (36,)),
# Box(float("-inf"), float("inf"), (378,)),
# Box(float("-inf"), float("inf"), (20,))
# ]
# ),
# action_space=TupleSpace([
# Box(-1.0, 1.0, (3,), dtype = np.float32),
# MultiDiscrete([2,2])
# ]
# )),
# }
config = (
PPOConfig()
.environment(
env="custom",
# "unity3d",
# env_config={
# "file_name": None,
# "episode_horizon": None,
# },
# disable_env_checking = True
)
.framework("torch")
# For running in editor, force to use just one Worker (we only have
# one Unity running)!
.rollouts(
num_rollout_workers=0,
rollout_fragment_length=200,
)
.training(
lr=0.0003,
lambda_=0.95,
gamma=0.99,
sgd_minibatch_size=256,
train_batch_size=4000,
num_sgd_iter=20,
clip_param=0.2,
model={"fcnet_hiddens": [512, 512]},
)
# .multi_agent(policies=policies,
# policy_mapping_fn=lambda agent_id, *args, **kwargs: "DodgeballAgent",)
# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
.resources(num_gpus=0)
)
# Switch on Curiosity based exploration for Pyramids env
# (not solvable otherwise).
stop = {
"training_iteration": args.stop_iters,
"timesteps_total": args.stop_timesteps,
"episode_reward_mean": args.stop_reward,
}
# Run the experiment.
results = tune.Tuner(
"PPO",
param_space=config.to_dict(),
run_config=air.RunConfig(
stop=stop,
verbose=1,
checkpoint_config=air.CheckpointConfig(
checkpoint_frequency=5,
checkpoint_at_end=True,
),
),
).fit()
# And check the results.
if args.as_test:
check_learning_achieved(results, args.stop_reward)
ray.shutdown()
If you still have problems, I would recommend creating a minimal repro script (something that doesn’t depend on anything other than gymnasium and rllib), so that we can better help.
@Jay,
So based on that error and looking at the Unity3DEnv. It seems to me that while rllib in general will handle mixed action space, that environment is not written to handle them.
Essentially what is happening there is actions contains the actions for all the agents that provided an observation in that stepand collected them in a list of np arrays.
The line that is erroring out us taking the first action and checking if it is a float. But because you are trying to use a tuple space, type does not have a dtype attribute.
If you look at all the bulyin spaces for that environment they are all Box or Multidiscrete so this makes sense.
To get this to work you are going to have to create your own version of the environment and add support for tuple action spaces.
