Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only
maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a …

In deep reinforcement learning, off-policy data help reduce on-policy interaction with the
environment, and the trust region policy optimization (TRPO) method is efficient to stabilize …

The development of autonomous agents that perform tasks with the same dexterity as
performed by humans is one of the challenges of artificial intelligence and robotics. This …

In reinforcement learning, importance sampling is a widely used method for evaluating an
expectation under the distribution of data of one policy when the data has in fact been …

Improving sample efficiency has been a longstanding goal in reinforcement learning. This
paper proposes VRMPO algorithm: a sample efficient policy gradient method with stochastic …

Safe reinforcement learning (RL) is still very challenging since it requires the agent to
consider both return maximization and safe exploration. In this paper, we propose CUP, a …

The policy-based reinforcement learning (RL) can be considered as maximization of its
objective. However, due to the inherent non-concavity of its objective, the policy gradient …

Abstract A Reinforcement Learning (RL) agent interacts with the environment to learn a
policy with high accumulated rewards through attempts and failures. However, RL suffers …