Search Results for author:
Found 9 papers, 5 papers with code
Domain Adaptive Imitation Learning with Visual Observation
In this paper, we consider domain-adaptive imitation learning with visual observation, where an agent in a target domain learns to perform a task by observing expert demonstrations in a source domain.
FoX: Formation-aware exploration in multi-agent reinforcement learning
Recently, deep multi-agent reinforcement learning (MARL) has gained significant popularity due to its success in various cooperative multi-agent tasks.
Robust Imitation Learning against Variations in Environment Dynamics
In this paper, we propose a robust imitation learning (IL) framework that improves the robustness of IL when environment dynamics are perturbed.
A Max-Min Entropy Framework for Reinforcement Learning
In this paper, we propose a max-min entropy framework for reinforcement learning (RL) to overcome the limitation of the soft actor-critic (SAC) algorithm implementing the maximum entropy RL in model-free sample-based learning.
A Reinforcement Learning Formulation of the Lyapunov Optimization: Application to Edge Computing Systems with Queue Stability
A condition for the reward function of reinforcement learning (RL) for queue stability is derived.
Cross-Domain Imitation Learning with a Dual Structure
In this paper, we consider cross-domain imitation learning (CDIL) in which an agent in a target domain learns a policy to perform well in the target domain by observing expert demonstrations in a source domain without accessing any reward function.
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
In this paper, sample-aware policy entropy regularization is proposed to enhance the conventional policy entropy regularization for better exploration.
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
In importance sampling (IS)-based reinforcement learning algorithms such as Proximal Policy Optimization (PPO), IS weights are typically clipped to avoid large variance in learning.
AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control
In this paper, a new adaptive multi-batch experience replay scheme is proposed for proximal policy optimization (PPO) for continuous action control.
