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montecarlo-pacman | A Monte Carlo tree search agent for Ms Pac-Man | Reinforcement Learning library

 by   sjmeverett Java Version: Current License: MIT

 by   sjmeverett Java Version: Current License: MIT

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kandi X-RAY | montecarlo-pacman Summary

montecarlo-pacman is a Java library typically used in Artificial Intelligence, Reinforcement Learning applications. montecarlo-pacman has no vulnerabilities, it has a Permissive License and it has low support. However montecarlo-pacman has 5 bugs and it build file is not available. You can download it from GitHub.
A Monte Carlo tree search agent for Ms Pac-Man.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • montecarlo-pacman has a low active ecosystem.
  • It has 19 star(s) with 6 fork(s). There are 3 watchers for this library.
  • It had no major release in the last 12 months.
  • montecarlo-pacman has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of montecarlo-pacman is current.
montecarlo-pacman Support
Best in #Reinforcement Learning
Average in #Reinforcement Learning
montecarlo-pacman Support
Best in #Reinforcement Learning
Average in #Reinforcement Learning

quality kandi Quality

  • montecarlo-pacman has 5 bugs (1 blocker, 2 critical, 1 major, 1 minor) and 56 code smells.
montecarlo-pacman Quality
Best in #Reinforcement Learning
Average in #Reinforcement Learning
montecarlo-pacman Quality
Best in #Reinforcement Learning
Average in #Reinforcement Learning

securitySecurity

  • montecarlo-pacman has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • montecarlo-pacman code analysis shows 0 unresolved vulnerabilities.
  • There are 7 security hotspots that need review.
montecarlo-pacman Security
Best in #Reinforcement Learning
Average in #Reinforcement Learning
montecarlo-pacman Security
Best in #Reinforcement Learning
Average in #Reinforcement Learning

license License

  • montecarlo-pacman is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
montecarlo-pacman License
Best in #Reinforcement Learning
Average in #Reinforcement Learning
montecarlo-pacman License
Best in #Reinforcement Learning
Average in #Reinforcement Learning

buildReuse

  • montecarlo-pacman releases are not available. You will need to build from source code and install.
  • montecarlo-pacman has no build file. You will be need to create the build yourself to build the component from source.
  • montecarlo-pacman saves you 545 person hours of effort in developing the same functionality from scratch.
  • It has 1276 lines of code, 99 functions and 21 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
montecarlo-pacman Reuse
Best in #Reinforcement Learning
Average in #Reinforcement Learning
montecarlo-pacman Reuse
Best in #Reinforcement Learning
Average in #Reinforcement Learning
Top functions reviewed by kandi - BETA

kandi has reviewed montecarlo-pacman and discovered the below as its top functions. This is intended to give you an instant insight into montecarlo-pacman implemented functionality, and help decide if they suit your requirements.

  • Runs the simulation .
    • Runs a batch of runs .
      • Gets the move towards the current node .
        • Randomly selects a node from a node
          • Runs the game agent .
            • Updates the total score of a given score .
              • Create a copy of this pacManagers .
                • Calculates the distance from the closest to the current game
                  • Calculates the distance to the current node in the game
                    • Run the additional evaluators .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      montecarlo-pacman Key Features

                      A Monte Carlo tree search agent for Ms Pac-Man

                      montecarlo-pacman Examples and Code Snippets

                      No Code Snippets are available at this moment for montecarlo-pacman.

                      See all Code Snippets related to Reinforcement Learning

                      Community Discussions

                      Trending Discussions on Reinforcement Learning
                      • Keras: AttributeError: 'Adam' object has no attribute '_name'
                      • What are vectorized environments in reinforcement learning?
                      • How does a gradient backpropagates through random samples?
                      • Relationship of Horizon and Discount factor in Reinforcement Learning
                      • OpenAI-Gym and Keras-RL: DQN expects a model that has one dimension for each action
                      • gym package not identifying ten-armed-bandits-v0 env
                      • ValueError: Input 0 of layer "max_pooling2d" is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: (None, 3, 51, 39, 32)
                      • Stablebaselines3 logging reward with custom gym
                      • What is the purpose of [np.arange(0, self.batch_size), action] after the neural network?
                      • DQN predicts same action value for every state (cart pole)
                      Trending Discussions on Reinforcement Learning

                      QUESTION

                      Keras: AttributeError: 'Adam' object has no attribute '_name'

                      Asked 2022-Apr-16 at 15:05

                      I want to compile my DQN Agent but I get error: AttributeError: 'Adam' object has no attribute '_name',

                      DQN = buildAgent(model, actions)
                      DQN.compile(Adam(lr=1e-3), metrics=['mae'])
                      

                      I tried adding fake _name but it doesn't work, I'm following a tutorial and it works on tutor's machine, it's probably some new update change but how to fix this

                      Here is my full code:

                      from keras.layers import Dense, Flatten
                      import gym
                      from keras.optimizer_v1 import Adam
                      from rl.agents.dqn import DQNAgent
                      from rl.policy import BoltzmannQPolicy
                      from rl.memory import SequentialMemory
                      
                      env = gym.make('CartPole-v0')
                      states = env.observation_space.shape[0]
                      actions = env.action_space.n
                      
                      episodes = 10
                      
                      def buildModel(statez, actiones):
                          model = Sequential()
                          model.add(Flatten(input_shape=(1, statez)))
                          model.add(Dense(24, activation='relu'))
                          model.add(Dense(24, activation='relu'))
                          model.add(Dense(actiones, activation='linear'))
                          return model
                      
                      model = buildModel(states, actions)
                      
                      def buildAgent(modell, actionz):
                          policy = BoltzmannQPolicy()
                          memory = SequentialMemory(limit=50000, window_length=1)
                          dqn = DQNAgent(model=modell, memory=memory, policy=policy, nb_actions=actionz, nb_steps_warmup=10, target_model_update=1e-2)
                          return dqn
                      
                      DQN = buildAgent(model, actions)
                      DQN.compile(Adam(lr=1e-3), metrics=['mae'])
                      DQN.fit(env, nb_steps=50000, visualize=False, verbose=1)
                      

                      ANSWER

                      Answered 2022-Apr-16 at 15:05

                      Your error came from importing Adam with from keras.optimizer_v1 import Adam, You can solve your problem with tf.keras.optimizers.Adam from TensorFlow >= v2 like below:

                      (The lr argument is deprecated, it's better to use learning_rate instead.)

                      # !pip install keras-rl2
                      import tensorflow as tf
                      from keras.layers import Dense, Flatten
                      import gym
                      from rl.agents.dqn import DQNAgent
                      from rl.policy import BoltzmannQPolicy
                      from rl.memory import SequentialMemory
                      
                      env = gym.make('CartPole-v0')
                      states = env.observation_space.shape[0]
                      actions = env.action_space.n
                      episodes = 10
                      
                      def buildModel(statez, actiones):
                          model = tf.keras.Sequential()
                          model.add(Flatten(input_shape=(1, statez)))
                          model.add(Dense(24, activation='relu'))
                          model.add(Dense(24, activation='relu'))
                          model.add(Dense(actiones, activation='linear'))
                          return model
                      
                      def buildAgent(modell, actionz):
                          policy = BoltzmannQPolicy()
                          memory = SequentialMemory(limit=50000, window_length=1)
                          dqn = DQNAgent(model=modell, memory=memory, policy=policy, 
                                         nb_actions=actionz, nb_steps_warmup=10, 
                                         target_model_update=1e-2)
                          return dqn
                      
                      model = buildModel(states, actions)
                      DQN = buildAgent(model, actions)
                      DQN.compile(tf.keras.optimizers.Adam(learning_rate=1e-3), metrics=['mae'])
                      DQN.fit(env, nb_steps=50000, visualize=False, verbose=1)
                      

                      Source https://stackoverflow.com/questions/71894769

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install montecarlo-pacman

                      You can download it from GitHub.
                      You can use montecarlo-pacman like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the montecarlo-pacman component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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