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Found 13 papers, 5 papers with code
Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning
Offline safe reinforcement learning (OSRL) involves learning a decision-making policy to maximize rewards from a fixed batch of training data to satisfy pre-defined safety constraints.
HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object Detection
Hence, in this paper, we present the radar-camera fusion network with Hybrid Generation and Synchronization (HGSFusion), designed to better fuse radar potentials and image features for 3D object detection.
Ranked #1 on
3D Object Detection (RoI)
on View-of-Delft (val)
3D Object Detection
3D Object Detection on View-of-Delft (val)
+4
Marvel: Accelerating Safe Online Reinforcement Learning with Finetuned Offline Policy
The high costs and risks involved in extensive environment interactions hinder the practical application of current online safe reinforcement learning (RL) methods.
Enhancing Safety in Reinforcement Learning with Human Feedback via Rectified Policy Optimization
To address this issue, we propose Rectified Policy Optimization (RePO), which replaces the expected safety constraint with critical safety constraints imposed on every prompt.
Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis
In this paper, we study reinforcement learning from human feedback (RLHF) under an episodic Markov decision process with a general trajectory-wise reward model.
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
In theory, we demonstrate that when the actor employs a no-regret optimization oracle, WSAC achieves a number of guarantees: (i) For the first time in the safe offline RL setting, we establish that WSAC can produce a policy that outperforms any reference policy while maintaining the same level of safety, which is critical to designing a safe algorithm for offline RL.
Safe Reinforcement Learning with Instantaneous Constraints: The Role of Aggressive Exploration
This paper studies safe Reinforcement Learning (safe RL) with linear function approximation and under hard instantaneous constraints where unsafe actions must be avoided at each step.
Model-Free, Regret-Optimal Best Policy Identification in Online CMDPs
PRI achieves trio objectives: (i) PRI is a model-free algorithm; and (ii) it outputs an approximately optimal policy with a high probability at the end of learning; and (iii) PRI guarantees $\tilde{\mathcal{O}}(H\sqrt{K})$ regret and constraint violation, which significantly improves the best existing regret bound $\tilde{\mathcal{O}}(H^4 \sqrt{SA}K^{\frac{4}{5}})$ under a model-free algorithm, where $H$ is the length of each episode, $S$ is the number of states, $A$ is the number of actions, and the total number of episodes during learning is $2K+\tilde{\cal O}(K^{0. 25}).$ We further present a matching lower via an example that shows under any online learning algorithm, there exists a well-separated CMDP instance such that either the regret or violation has to be $\Omega(H\sqrt{K}),$ which matches the upper bound by a polylogarithmic factor.
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a cumulative constraint on the expected utility (cost).
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems
The proposed algorithm is distributed in two aspects: (i) the learned policy is a distributed policy that maps a local state of an agent to its local action and (ii) the learning/training is distributed, during which each agent updates its policy based on its own and neighbors' information.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processes
This paper presents the first model-free, simulator-free reinforcement learning algorithm for Constrained Markov Decision Processes (CMDPs) with sublinear regret and zero constraint violation.
FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
In this paper, we propose a new type of Actor, named forward-looking Actor or FORK for short, for Actor-Critic algorithms.
QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection
The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation.
