Search Results for author:
Found 10 papers, 4 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.
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning
In this paper, we propose a new mutual information framework for multi-agent reinforcement learning to enable multiple agents to learn coordinated behaviors by regularizing the accumulated return with the simultaneous mutual information between multi-agent actions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability
This paper proposes a framework, named Quantile Constrained RL (QCRL), to constrain the quantile of the distribution of the cumulative sum cost that is a necessary and sufficient condition to satisfy the outage constraint.
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
In this paper, we consider cooperative multi-agent reinforcement learning (MARL) with sparse reward.
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.
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.
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning
In this paper, we propose a maximum mutual information (MMI) framework for multi-agent reinforcement learning (MARL) to enable multiple agents to learn coordinated behaviors by regularizing the accumulated return with the mutual information between actions.
Multiagent Systems
Population-Guided Parallel Policy Search for Reinforcement Learning
In the proposed scheme, multiple identical learners with their own value-functions and policies share a common experience replay buffer, and search a good policy in collaboration with the guidance of the best policy information.
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.
Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces
In this paper, a new interactive parallel learning scheme is proposed to enhance the performance of off-policy continuous-action reinforcement learning.
