reinforcement-learning

问题 I am trying to train a Reinforcement Learning agent using TF-Agent TF-Agent DQN Tutorial. In my application, I have 1 action containing 9 possible discrete values (labeled from 0 to 8). Below is the output from env.action_spec() BoundedTensorSpec(shape=(), dtype=tf.int64, name='action', minimum=array(0, dtype=int64), maximum=array(8, dtype=int64)) I would like to get the probability vector contains all actions calculated by the trained policy, and do further processing in other application

问题 Here is the original script I'm trying to run on both CPU and GPU, I'm expecting a much faster training on GPU however it's taking almost the same time. I made the following modification to main() (the first 4 lines) because the original script does not activate / use the GPU. Suggestions ... ? def main(): physical_devices = tf.config.experimental.list_physical_devices('GPU') if len(physical_devices) > 0: tf.config.experimental.set_memory_growth(physical_devices[0], True) print('GPU activated

问题 Here is the original script I'm trying to run on both CPU and GPU, I'm expecting a much faster training on GPU however it's taking almost the same time. I made the following modification to main() (the first 4 lines) because the original script does not activate / use the GPU. Suggestions ... ? def main(): physical_devices = tf.config.experimental.list_physical_devices('GPU') if len(physical_devices) > 0: tf.config.experimental.set_memory_growth(physical_devices[0], True) print('GPU activated

问题 I want to setup an RL agent on the OpenAI CarRacing-v0 environment, but before that I want to understand the action space. In the code on github line 119 says: self.action_space = spaces.Box( np.array([-1,0,0]), np.array([+1,+1,+1])) # steer, gas, brake How do I read this line? Although my problem is concrete wrt CarRacing-v0 I would like to understand the spaces.Box() notation in general 回答1: Box means that you are dealing with real valued quantities. The first array np.array([-1,0,0] are

问题 I want to setup an RL agent on the OpenAI CarRacing-v0 environment, but before that I want to understand the action space. In the code on github line 119 says: self.action_space = spaces.Box( np.array([-1,0,0]), np.array([+1,+1,+1])) # steer, gas, brake How do I read this line? Although my problem is concrete wrt CarRacing-v0 I would like to understand the spaces.Box() notation in general 回答1: Box means that you are dealing with real valued quantities. The first array np.array([-1,0,0] are

来源： https://stackoverflow.com/questions/61526437/deep-q-learning-without-openai-gym

来源： https://stackoverflow.com/questions/61526437/deep-q-learning-without-openai-gym

来源： https://stackoverflow.com/questions/63072770/pytoch-runtimeerror-dimension-out-of-range-expected-to-be-in-range-of-1-0

问题 I followed this link to doc to create environment of my own. But when i run this from mlagents_envs.environment import UnityEnvironment env = UnityEnvironment(file_name="v1-ball-cube-game.x86_64") env.reset() behavior_names = env.behavior_spec.keys() print(behavior_names) Game window pop up and then terminal show error saying Traceback (most recent call last): File "index.py", line 6, in <module> behavior_names = env.behavior_spec.keys() AttributeError: 'UnityEnvironment' object has no

问题 I am trying to use a keras (version 2.2.50) neural network / sequential model to create a simple agent in a reinforcement learning setting using the reinforcelearn package (version 0.2.1) according to this vignette: https://cran.r-project.org/web/packages/reinforcelearn/vignettes/agents.html . This is the code I use: library('reinforcelearn') library('keras') model = keras_model_sequential() %>% layer_dense(units = 10, input_shape = 4, activation = "linear") %>% compile(optimizer = optimizer