9 Population synthesis without microdata.7.5 Comparing methods of reweighting large datasets.6.4 The Urban Data Science Toolkit (UDST).6.2 Population synthesis as an optimization problem.6 Alternative approaches to population synthesis.5.6.3 Comparing the results for SimpleWorld.5.6.2 Comparing the weights for SimpleWorld.4.7 ‘Flattening’ the individual level data.4.6 Matching individual and aggregate level data names.4.5 Re-categorising individual level variables.4.4 Subsetting to remove excess information.3.2 What spatial microsimulation is not.3.1.2 Spatial microsimulation: method or approach?.3.1.1 Spatial microsimulation as SimCity.2.1 Getting setup with the RStudio environment.
2 SimpleWorld: A worked example of spatial microsimulation.1.5 Why spatial microsimulation with R?.1.3 A definition of spatial microsimulation.1.1 Who this book is for and how to use it.Memory=SequentialMemory(limit=100000,window_length=1)Īgent=DQNAgent(model=model1,memory=memory,policy=policy,nb_actions=actions,nb_steps_warmup=500, target_model_update=1e-2)Īpile(Adam(lr=0.001),metrics=)Īgent. Model.add(Dense(actions,activation='linear')) Model.add(Embedding(states,10, input_length=1)) I am wondering if the states in taxi problem is just a scalar (500), not like cart-pole has a state of an array with 4 elements? Please help or a little advise will help a lot, also if you can help me to extend the steps more than 200 is better!!(env._max_episode_steps=5000) #import environment and visualizationįrom import Model, Sequentialįrom import Dense, Flatten, Input, Embedding,Reshapeįrom import Adam I tried to run the code to Cart Pole problem there's no error came out. ValueError: The truth value of an array with more than one element is ambiguous. usr/local/lib/python3.8/dist-packages/rl/core.py in fit(self, env, nb_steps, action_repetition, callbacks, verbose, visualize, nb_max_start_steps, start_step_policy, log_interval, nb_max_episode_steps)ġ79 observation, r, done, info = _step(observation, r, done, info) ValueError Traceback (most recent call last)ġ pile(Adam(lr=0.001),metrics=) I am trying to use keras-rl2 DQNAgent to solve the taxi problem in open AI Gym.įor a quick refresh, please find it in Gym-Documentation, thank you!ġ.Build the deep learning model by keras Sequential API with Embedding and Dense layersĢ.Import the Epsilon Greedy policy and Sequential Memory deque from keras-rl2's rlģ.input the model, policy, and the memory in to rl.agent.DQNAgent and compile the modelīut when i fit the model(agent) the error pops up: Training for 1000000 steps.
0 kommentar(er)
