Metadata-Version: 2.1
Name: pytorch-rllib
Version: 0.1.0
Summary: Reinforcement Learning Library.
Home-page: https://github.com/dayyass/rllib
Author: Dani El-Ayyass
Author-email: dayyass@yandex.ru
License: UNKNOWN
Description: # rllib
        Reinforcement Learning Library.
        
        ## Installation
        ```
        pip install pytorch-rllib
        ```
        
        ## Usage
        Implemented agents:
        - [ ] CrossEntropy
        - [ ] Value / Policy Iteration
        - [x] Q-Learning
        - [ ] SARSA
        - [ ] DQN
        - [ ] Rainbow
        - [ ] REINFORCE
        - [ ] A2C
        
        ```python3
        import gym
        import numpy as np
        
        from rllib.qlearning import QLearningAgent
        from rllib.trainer import Trainer
        from rllib.utils import set_global_seed
        
        # make environment
        env = gym.make("Taxi-v3")
        set_global_seed(seed=42, env=env)
        
        n_actions = env.action_space.n
        
        # make agent
        agent = QLearningAgent(
            alpha=0.5,
            epsilon=0.25,
            discount=0.99,
            n_actions=n_actions,
        )
        
        # train
        trainer = Trainer(env=env)
        rewards = trainer.train(
            agent=agent,
            n_sessions=1000,
        )
        
        print(f"Mean reward: {np.mean(rewards[-10:])}")  # Mean reward: 8.0
        ```
        
        More examples you can find [here](examples).
        
        ## Requirements
        Python >= 3.7
        
        ## Citation
        If you use **rllib** in a scientific publication, we would appreciate references to the following BibTex entry:
        ```bibtex
        @misc{dayyass2022rllib,
            author       = {El-Ayyass, Dani},
            title        = {Reinforcement Learning Library},
            howpublished = {\url{https://github.com/dayyass/rllib}},
            year         = {2022}
        }
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
