Implementation of some popular reinforcement learning models
Project 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
pip install rlmodels
Usage
Below is a summary of how the program works. To see the full documentation click here
Initialization
The following is an example with the popular CartPole environment using a double Q network. First the setup
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
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
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.
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
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
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