I am trying to set a Deep-Q-Learning agent with a custom environment in OpenAI Gym. I have 4 continuous state variables with individual limits and 3 integer action variables with individual limits.
Here is the code:
#%% import
from gym import Env
from gym.spaces import Discrete, Box, Tuple
import numpy as np
#%%
class Custom_Env(Env):
def __init__(self):
# Define the state space
#State variables
self.state_1 = 0
self.state_2 = 0
self.state_3 = 0
self.state_4_currentTimeSlots = 0
#Define the gym components
self.action_space = Box(low=np.array([0, 0, 0]), high=np.array([10, 20, 27]), dtype=np.int)
self.observation_space = Box(low=np.array([20, -20, 0, 0]), high=np.array([22, 250, 100, 287]),dtype=np.float16)
def step(self, action ):
# Update state variables
self.state_1 = self.state_1 + action [0]
self.state_2 = self.state_2 + action [1]
self.state_3 = self.state_3 + action [2]
#Calculate reward
reward = self.state_1 + self.state_2 + self.state_3
#Set placeholder for info
info = {}
#Check if it's the end of the day
if self.state_4_currentTimeSlots >= 287:
done = True
if self.state_4_currentTimeSlots < 287:
done = False
#Move to the next timeslot
self.state_4_currentTimeSlots +=1
state = np.array([self.state_1,self.state_2, self.state_3, self.state_4_currentTimeSlots ])
#Return step information
return state, reward, done, info
def render (self):
pass
def reset (self):
self.state_1 = 0
self.state_2 = 0
self.state_3 = 0
self.state_4_currentTimeSlots = 0
state = np.array([self.state_1,self.state_2, self.state_3, self.state_4_currentTimeSlots ])
return state
#%% Set up the environment
env = Custom_Env()
#%% Create a deep learning model with keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam
def build_model(states, actions):
model = Sequential()
model.add(Dense(24, activation='relu', input_shape=states))
model.add(Dense(24, activation='relu'))
model.add(Dense(actions[0] , activation='linear'))
return model
states = env.observation_space.shape
actions = env.action_space.shape
print("env.observation_space: ", env.observation_space)
print("env.observation_space.shape : ", env.observation_space.shape )
print("action_space: ", env.action_space)
print("action_space.shape : ", env.action_space.shape )
model = build_model(states, actions)
print(model.summary())
#%% Build Agent wit Keras-RL
from rl.agents import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
def build_agent (model, actions):
policy = BoltzmannQPolicy()
memory = SequentialMemory(limit = 50000, window_length=1)
dqn = DQNAgent (model = model, memory = memory, policy=policy,
nb_actions=actions, nb_steps_warmup=10, target_model_update= 1e-2)
return dqn
dqn = build_agent(model, actions)
dqn.compile(Adam(lr=1e-3), metrics = ['mae'])
dqn.fit (env, nb_steps = 4000, visualize=False, verbose = 1)
When I run this code I get the following error message
ValueError: Model output "Tensor("dense_23/BiasAdd:0", shape=(None, 3), dtype=float32)" has invalid shape. DQN expects a model that has one dimension for each action, in this case (3,).
thrown by the line dqn = DQNAgent (model = model, memory = memory, policy=policy, nb_actions=actions, nb_steps_warmup=10, target_model_update= 1e-2)
Can anyone tell me, why this problem is occuring and how to solve this issue? I assume it has something to do with the built model and thus with the action and state spaces. But I could not figure out what exactly the problem is.
Reminder on the bounty: My bounty is expiring quite soon and unfortunately, I still have not received any answer. If you at least have a guess how to tackle that problem, I'll highly appreciate if you share your thoughts with me and I would be quite thankful for it.
As we talked about in the comments, it seems that the Keras-rl library is no longer supported (the last update in the repository was in 2019), so it's possible that everything is inside Keras now. I take a look at Keras documentation and there are no high-level functions to build a reinforcement learning model, but is possible to use lower-level functions to this.
Another solution may be to downgrade to Tensorflow 1.0 as it seems the compatibility problem occurs due to some changes in version 2.0. I didn't test, but maybe the Keras-rl + Tensorflow 1.0 may work.
There is also a branch of Keras-rl to support Tensorflow 2.0, the repository is archived, but there is a chance that it will work for you