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Found 16 papers, 6 papers with code
No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learning
The large action space is one fundamental obstacle to deploying Reinforcement Learning methods in the real world.
Never Explore Repeatedly in Multi-Agent Reinforcement Learning
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning Diverse Risk Preferences in Population-based Self-play
In this paper, we aim to introduce diversity from the perspective that agents could have diverse risk preferences in the face of uncertainty.
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning
Self-supervised methods have become crucial for advancing deep learning by leveraging data itself to reduce the need for expensive annotations.
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery
We show that such lossless primitives can drastically improve the performance of hierarchical policies.
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation
Among the reasons hindering reinforcement learning (RL) applications to real-world problems, two factors are critical: limited data and the mismatch between the testing environment (real environment in which the policy is deployed) and the training environment (e. g., a simulator).
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning
Keeping risk under control is often more crucial than maximizing expected rewards in real-world decision-making situations, such as finance, robotics, autonomous driving, etc.
On the Role of Discount Factor in Offline Reinforcement Learning
The discount factor, $\gamma$, plays a vital role in improving online RL sample efficiency and estimation accuracy, but the role of the discount factor in offline RL is not well explored.
Offline Reinforcement Learning with Value-based Episodic Memory
Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data.
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings
Head detection in the indoor video is an essential component of building occupancy detection.
Average-Reward Reinforcement Learning with Trust Region Methods
Our work provides a unified framework of the trust region approach including both the discounted and average criteria, which may complement the framework of reinforcement learning beyond the discounted objectives.
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint.
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning
Value-based methods of multi-agent reinforcement learning (MARL), especially the value decomposition methods, have been demonstrated on a range of challenging cooperative tasks.
SOAC: The Soft Option Actor-Critic Architecture
In these tasks, our approach learns a diverse set of options, each of whose state-action space has strong coherence.
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning
In this paper, we present a new reinforcement learning (RL) algorithm called Distributional Soft Actor Critic (DSAC), which exploits the distributional information of accumulated rewards to achieve better performance.
