Search Results for author:
Found 6 papers, 2 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.
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.
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems.
Partially Observable Reinforcement Learning
reinforcement-learning
+1
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning
In this paper, we consider intrinsic reward generation for sparse-reward reinforcement learning based on model prediction errors.
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.
Model Ensemble-Based Intrinsic Reward for Sparse Reward Reinforcement Learning
In this paper, a new intrinsic reward generation method for sparse-reward reinforcement learning is proposed based on an ensemble of dynamics models.
