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Maximum Entropy Reinforcement Learning with Mixture Policies
However, using mixture policies in the Maximum Entropy (MaxEnt) framework is not straightforward.
Action Redundancy in Reinforcement Learning
Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning paradigm which seeks to maximize return under entropy regularization.
Partial Simulation for Imitation Learning
Model-based imitation learning methods require full knowledge of the transition kernel for policy evaluation.
Inspiration Learning through Preferences
We denote this setup as \textit{Inspiration Learning} - knowledge transfer between agents that operate in different action spaces.
End-to-End Differentiable Adversarial Imitation Learning
Generative Adversarial Networks (GANs) have been successfully applied to the problem of policy imitation in a model-free setup.
Model-based Adversarial Imitation Learning
A model-based approach for the problem of adversarial imitation learning.
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance.
Deep Reinforcement Learning Discovers Internal Models
Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in challenging problems such as playing Atari, solving Go and controlling robots.
