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dennybritz / reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
http://www.wildml.com/2016/10/learning-reinforcement-learning/
MIT License
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Reinforcement learning policy #238

Open Comp-Engr18 opened 3 years ago

Comp-Engr18 commented 3 years ago

I want to make a project using reinforcement learning in which a bot send scam to other bots on social media, other bots detect the scam and reject it. I think it needs a deep reinforcement learning.

I have seen projects of games in which environments are already build in e.g., ​like cartpole game. But for my project mentioned above do I need to use any build in environment and build in policy?

If so please tell which policy and environment can I choose? If I need to build my own policy and existing policies and environments cannot work in my case, can anyone share me code and tutorial where I can learn how to build own policies.

Is it necessary to build environment in reinforcement learning or we can work without environment as well?

Moreover, can I use actor critic policy in this case? Is every actor critic policy has to be modified for each different project?

I want to know the answers of these questions to have clear understanding.

AbhinavSharma07 commented 7 months ago

For your project involving scam detection on social media using reinforcement learning, you'll likely need to define your own custom environment to simulate the interactions between the scam bots and the detection bots. Since this scenario doesn't fit into pre-existing environments like those used for games, you'll need to create one tailored to your problem.

As for policies, you have several options. You can use existing policies such as DQN (Deep Q-Network), PPO (Proximal Policy Optimization), or actor-critic methods like A2C (Advantage Actor-Critic) or A3C (Asynchronous Advantage Actor-Critic). Actor-critic methods can be suitable for this task as they combine the benefits of both policy-based and value-based methods, allowing for more stable training.

Building your own policy isn't necessarily required if existing ones suit your needs. However, if you decide to create a custom policy, you can find resources online for learning how to design and implement your own reinforcement learning algorithms. OpenAI's Spinning Up in Deep Reinforcement Learning is a popular resource that provides code and tutorials for developing custom policies and algorithms.

In reinforcement learning, the environment is crucial as it defines the task and interactions the agent experiences. Since your project involves social media interactions, you'll need to build a custom environment to simulate these interactions realistically.

Regarding actor-critic policies, they can be adapted to different projects, but the specific modifications required will depend on the characteristics of your environment and the requirements of your task. You may need to tune hyperparameters, adjust network architectures, or incorporate domain-specific knowledge to achieve optimal performance.

In short :

  1. You'll likely need to build a custom environment for your project.
  2. Existing policies such as DQN, PPO, and actor-critic methods can be suitable.
  3. You can find resources for learning how to build custom policies if needed.
  4. Actor-critic policies can be adapted to different projects but may require modifications based on specific requirements.