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Found 3 papers, 1 papers with code
Value Improved Actor Critic Algorithms
In this work, we propose a general extension to the AC framework that employs two separate improvement operators: one applied to the policy in the spirit of policy-based algorithms and one applied to the value in the spirit of value-based algorithms, which we dub Value-Improved AC (VI-AC).
Diverse Projection Ensembles for Distributional Reinforcement Learning
In contrast to classical reinforcement learning, distributional reinforcement learning algorithms aim to learn the distribution of returns rather than their expected value.
Safe Continuous Control with Constrained Model-Based Policy Optimization
Further, we provide theoretical and empirical analyses regarding the implications of model-usage on constrained policy optimization problems and introduce a practical algorithm that accelerates policy search with model-generated data.
