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Found 22 papers, 3 papers with code
Masked Autoencoders in 3D Point Cloud Representation Learning
We first split the input point cloud into patches and mask a portion of them, then use our Patch Embedding Module to extract the features of unmasked patches.
A Multi-Objective Deep Reinforcement Learning Framework
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks.
Multi-Objective Reinforcement Learning
reinforcement-learning
A Survey of Multi-Objective Sequential Decision-Making
Using this taxonomy, we survey the literature on multi-objective methods for planning and learning.
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions
We report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state transitions.
Discrete-to-Deep Supervised Policy Learning
It then selects from each discrete state an input value and the action with the highest numerical preference as an input/target pair.
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario
As a way to explain the goal-driven robot's actions, we use the probability of success computed by three different proposed approaches: memory-based, learning-based, and introspection-based.
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process.
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment
We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL).
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning
When interacting with a learner agent, humans may provide either evaluative or informative advice.
Persistent Rule-based Interactive Reinforcement Learning
Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-time.
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives.
Levels of explainable artificial intelligence for human-aligned conversational explanations
Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML).
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey
EXplainable RL (XRL) is relatively recent field of research that aims to develop techniques to extract concepts from the agent's: perception of the environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and objectives.
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task
Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users.
A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments
However, current research has been limited to interactions that offer actionable advice to only the current state of the agent.
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios
In this work, we make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action.
Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks
However, divergent and unstable behaviour have been long standing issues in DQNs.
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios
The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents.
Weighted Point Cloud Normal Estimation
Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures.
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments
One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility function.
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards.
Multi-Objective Reinforcement Learning
reinforcement-learning
Value function interference and greedy action selection in value-based multi-objective reinforcement learning
Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives, represented by vector-valued rewards.
