Metadata-Version: 2.1
Name: rlmodels
Version: 1.0.3
Summary: Implementation of some popular reinforcement learning models
Home-page: https://github.com/nestorsag/rlmodels
Author: Nestor Sanchez
Author-email: nestor.sag@gmail.com
License: MIT
Download-URL: https://github.com/nestorSag/rlmodels/archive/1.0.3.tar.gz
Description: # rlmodels: Out-of-the-box reinforcement learning
        
        This project is a collection of some popular optimisation algorithms for reinforcement learning problem. At the moment the available models are:
        
        * Double Q network with prioritazed experience replay (PER)
        * Covariance matrix adaptive evolutionary strategy (CMAES)
        
        with some more going to be added in the future.
        
        It works with Pytorch models and environment classes like the OpenAI gym ones. Any environment class wrapper that mimic their basic functionality should be fine, but more on that below.
        
        ## Getting Started
        
        ### Prerequisites
        
        The project uses ```python 3.6``` and ```torch 1.1.0```.
        
        ### Installing
        
        It can be installed directly from pip like 
        ```bash
        pip install rlmodels
        ```
        
        ## Usage
        
        Below is a summary of how the program works. **To see the full documentation click [here](https://nestorsag.github.io/rlmodels/index.html#package)**
        
        ### Initialization
        
        The following is an example with the popular CartPole environment using a double Q network. First the setup
        
        ```python
        
        from rlmodels.models.grad import DoubleQNetwork
        from rlmodels.nets import VanillaNet
        import gym
        ```
        
        The models are divided in evolutionary strategies (es) and gradient-based ones (grad). The library also has a basic network definition, VanillaNet, to which we only need to specify number and size of hidden layer, input and output sizes, and last activation function. It uses ReLU everywhere else by default.
        
        let's create the basic objects 
        
        ```python
        env = gym.make('CartPole-v0')
        
        ##make it reproducible
        env.seed(1)
        np.random.seed(1)
        torch.manual_seed(1)
        
        agent = VanillaNet([60],4,2,None)
        target = VanillaNet([60],4,2,None)
        
        ddq = DoubleQNetwork(agent,target,env)
        ```
        
        Now we can fit the agent
        
        ```python
        ddq.fit(n_episodes=1000,
        	max_ts_by_episode=200,
        	batch_size=lambda t: 200,
        	exploration_rate_func = lambda t: max(0.01,0.05 - 0.01*int(t/2500)), #decrease exploration down to 1% after 10,000 steps
        	max_memory_size=2000,
        	learning_rate=0.001,
        	tau=lambda t: 100,
        	scheduler_func=lambda t: 1.25**(-int(t/2500)), #decrease step size a bit every 2,500 steps
        	verbose=True)
        ```
        
        Almost all arguments receive a function that maps number of elapsed timesteps to parameter values, to allow for dynamic tunning, for example to decrease stepsize and exploration rate after a fixed number of steps, as above.
        
        Once the agent is trained we can visualize the reward trace. If we are using an environment with a render method (like OpenAI ones) we can also visualise the trained agent.
        
        ```python
        ddq.plot()
        ddq.play(n=200)
        ```
        
        see the ```example``` folder for an analogous use of CMAES.
        
        ### Environment
        For custom environments or custom rewards, its possible to make a wrapper tha mimics te behavior of the step() and reset() function of gym's environemnts
        ```python
        class MyCustomEnv(object):
        	def __init__(self,env):
        		self.env = env
        	def step(self,action):
        		## get next state s, reward, and termination flag (boolean), and any additional info
        		return s,r, terminated, info #need to output these 4 things (info can be None)
        	def reset(self):
        		#something
        	def seed(self):
        		#something
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
