Soft Actor-Critic Algorithms and Applications
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Code (52)
Tasks
Decision Makingreinforcement-learningReinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SACn: Soft Actor-Critic with n-step Returns
Soft Actor-Critic (SAC) is widely used in practical applications and is now one of the most relevant off-policy online model-free reinforcement learning (RL) methods. The technique of n-step returns is known to increase …
Reinforcement LearningBounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm
One of the bottlenecks preventing Deep Reinforcement Learning algorithms (DRL) from real-world applications is how to explore the environment and collect informative transitions efficiently. The present paper describes b…
Deep Reinforcement LearningWasserstein Barycenter Soft Actor-Critic
Deep off-policy actor-critic algorithms have emerged as the leading framework for reinforcement learning in continuous control domains. However, most of these algorithms suffer from poor sample efficiency, especially in …
continuous-controlContinuous ControlMuJoCoSoft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL
Discrete reinforcement learning (RL) algorithms have demonstrated exceptional performance in solving sequential decision tasks with discrete action spaces, such as Atari games. However, their effectiveness is hindered wh…
Atari Gamescontinuous-controlContinuous ControlReinforcement Learning (RL)Regularization of Soft Actor-Critic Algorithms with Automatic Temperature Adjustment
This work presents a comprehensive analysis to regularize the Soft Actor-Critic (SAC) algorithm with automatic temperature adjustment. The the policy evaluation, the policy improvement and the temperature adjustment are …