cannot run interactive.py about multiagent-particle-envs

[SweetPin]

This may be a problem with the gym version.You try to change the gym version to 0.10.5 and see if it works.

[indhra]

remove
from gym.spaces import prng
and replace
random_array = prng.np_random.rand(self.num_discrete_space)
with

random_array = np.random.RandomState().rand(self.num_discrete_space)

@christopherhesse
please close

[christopherhesse]

This repo should probably have the correct gym version required in https://github.com/openai/multiagent-particle-envs/blob/master/setup.py#L12 instead of just choosing the latest.

[AmulyaReddy99]

@christopherhesse
thanks for quick reply
can u help me out with the following code

OBSERVATION SPACE IS GIVING ERROR

import numpy as np
import gym

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam

from rl.agents.dqn import DQNAgent
from rl.policy import EpsGreedyQPolicy
from rl.memory import SequentialMemory

Building the environment

env = make_env('simple_adversary')

np.random.seed(0)
env.seed(0)

Extracting the number of possible actions

num_actions = env.action_space[0].n # first agent actions changing this vaue will be another agent
print(num_actions)

Layers

agent = Sequential()

print(env.observation_space[0].shape,type(env.observation_space[0]))

agent.add(Flatten(input_shape =(1, ) + (env.observation_space[0]).shape))

agent.add(Dense(16))
agent.add(Activation('relu'))
agent.add(Dense(num_actions))
agent.add(Activation('linear'))
agent.summary()

Building model

strategy = EpsGreedyQPolicy()
memory = SequentialMemory(limit = 10000, window_length = 1)
dqn = DQNAgent(model = agent, nb_actions = num_actions,
memory = memory, nb_steps_warmup = 10, target_model_update = 1e-2, policy = strategy)
dqn.compile(Adam(lr = 1e-3), metrics =['mae'])

Visualizing the training

dqn.fit(env, nb_steps = 5000, visualize = False, verbose = 2)

testing

dqn.test(env, nb_episodes = 5, visualize = False)

Please use the below for loading env

import multiagent.scenarios as scenarios
scenario = scenarios.load('simple_adversary.py').Scenario()
world = scenario.make_world()
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, info_callback=None, shared_viewer = False)

Now, you can proceed with the code for adding neural networks.

Hope this helps

[Privilger]

From README of gym:

2019-02-06 (v0.11.0)
remove gym.spaces.np_random common PRNG; use per-instance PRNG instead.

[ZechenLiu]

Have you solved it yet?
I'm also working on the same problem. My gym version is v0.14.0 , my python version is 3.6.x. Are those versions over high that will forbid running it ?

[canyon]

Have you solved it yet?
I'm also working on the same problem. My gym version is v0.14.0 , my python version is 3.6.x. Are those versions over high that will forbid running it ?

i can run it with the gym version at 0.10.5.

[zhaolongkzz]

change to an old version (0.10.5) is ok, you'd better change like this #53

[indhra]

@christopherhesse
thanks for quick reply
can u help me out with the following code

OBSERVATION SPACE IS GIVING ERROR

import numpy as np
import gym

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam

from rl.agents.dqn import DQNAgent
from rl.policy import EpsGreedyQPolicy
from rl.memory import SequentialMemory

Building the environment

env = make_env('simple_adversary')

np.random.seed(0)
env.seed(0)

Extracting the number of possible actions

num_actions = env.action_space[0].n # first agent actions changing this vaue will be another agent
print(num_actions)

Layers

agent = Sequential()

print(env.observation_space[0].shape,type(env.observation_space[0]))

agent.add(Flatten(input_shape =(1, ) + (env.observation_space[0]).shape))

agent.add(Dense(16))
agent.add(Activation('relu'))
agent.add(Dense(num_actions))
agent.add(Activation('linear'))
agent.summary()

Building model

strategy = EpsGreedyQPolicy()
memory = SequentialMemory(limit = 10000, window_length = 1)
dqn = DQNAgent(model = agent, nb_actions = num_actions,
memory = memory, nb_steps_warmup = 10, target_model_update = 1e-2, policy = strategy)
dqn.compile(Adam(lr = 1e-3), metrics =['mae'])

Visualizing the training

dqn.fit(env, nb_steps = 5000, visualize = False, verbose = 2)

testing

dqn.test(env, nb_episodes = 5, visualize = False)

[AmulyaReddy99]

Have you solved it yet?
I'm also working on the same problem. My gym version is v0.14.0 , my python version is 3.6.x. Are those versions over high that will forbid running it ?

I am using same versions gym==0.14.0 and python3.6. It is working fine for me.