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Found 10 papers, 0 papers with code
Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation
However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the models learned in simulations to new domains in the real world.
Model-Based Imitation Learning Using Entropy Regularization of Model and Policy
We derive structured discriminators so that the learning of the policy and the model is efficient.
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning
Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entropy and sparse entropy.
$q$-Munchausen Reinforcement Learning
The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the current stochastic policy.
Forward and inverse reinforcement learning sharing network weights and hyperparameters
A forward RL step minimizes the reverse KL estimated by the inverse RL step.
Unbounded Output Networks for Classification
In this study, by adopting features of the EE-RBM approach to feed-forward neural networks, we propose the UnBounded output network (UBnet) which is characterized by three features: (1) unbounded output units; (2) the target value of correct classification is set to a value much greater than one; and (3) the models are trained by a modified mean-squared error objective.
Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming
Approximate dynamic programming algorithms, such as approximate value iteration, have been successfully applied to many complex reinforcement learning tasks, and a better approximate dynamic programming algorithm is expected to further extend the applicability of reinforcement learning to various tasks.
Online Meta-learning by Parallel Algorithm Competition
In the OMPAC method, several instances of a reinforcement learning algorithm are run in parallel with small differences in the initial values of the meta-parameters.
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
First, we propose two activation functions for neural network function approximation in reinforcement learning: the sigmoid-weighted linear unit (SiLU) and its derivative function (dSiLU).
A Generalized Natural Actor-Critic Algorithm
In this paper, we describe a generalized Natural Gradient (gNG) by linearly interpolating the two FIMs and propose an efficient implementation for the gNG learning based on a theory of the estimating function, generalized Natural Actor-Critic (gNAC).
