mean-conc-beta
Beta distribution parameterized by mean and concentration for bounded continuous action spaces in reinforcement learning.
Install
$ pip install mean-conc-beta
Usage
import torch
import torch.nn.functional as F
from mean_conc_beta import Beta
beta = Beta()
# network output: (batch, num_actions, 2) for raw mean and concentration
params = torch.randn(16, 4, 2, requires_grad = True)
# distribution on (-1, 1)
dist = beta(params)
# sample actions
actions = dist.sample()
actions_reparam = dist.rsample()
# log prob and entropy
log_prob = beta.log_prob(dist, actions)
entropy = beta.entropy(dist)
# behavior cloning with mse loss on mean
expert_actions = torch.rand(16, 4)
pred_mean = beta.mean(params)
bc_loss = F.mse_loss(pred_mean, expert_actions)
bc_loss.backward()
Citations
@article{Ferrari2004BetaRF,
title = {Beta Regression for Modelling Rates and Proportions},
author = {Silvia L. P. Ferrari and Francisco Cribari-Neto},
journal = {Journal of Applied Statistics},
year = {2004},
volume = {31},
pages = {799 - 815}
}
@inproceedings{Chou2017TheBP,
title = {The Beta Policy for Continuous Reinforcement Learning},
author = {Po-Wei Chou and Daniel Maturana and Sebastian Scherer},
booktitle = {International Conference on Machine Learning},
year = {2017}
}
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