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Found 11 papers, 3 papers with code
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth
Communication plays a vital role in multi-agent systems, fostering collaboration and coordination.
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem
Many instances of similar or almost-identical industrial machines or tools are often deployed at once, or in quick succession.
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning
Then, to ensure that the rules explain a valid, non-degenerate policy, we introduce a refinement algorithm that fine-tunes the rules to obtain good performance when executed in the environment.
Explainable Reinforcement Learning Through Goal-Based Explanations
Our key contribution to improve explainability is introducing goal-based explanations, a new explanation mechanism where the agent produces goals and attempts to reach those goals one-by-one while maximizing the collected reward.
Transfer Learning Across Simulated Robots With Different Sensors
After training in the lab, the robot should be able to get by without the expensive equipment that used to be available to it, and yet still be guaranteed to perform well on the field.
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
We argue that actor-critic algorithms are limited by their need for an on-policy critic.
The Actor-Advisor: Policy Gradient With Off-Policy Advice
In this paper, we propose an elegant solution, the Actor-Advisor architecture, in which a Policy Gradient actor learns from unbiased Monte-Carlo returns, while being shaped (or advised) by the Softmax policy arising from an off-policy critic.
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) algorithm by Natarajan and Tadepalli (2005), are required.
Multi-Objective Reinforcement Learning
reinforcement-learning
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
Our technique, Directed Policy Gradient (DPG), allows a teacher or backup policy to override the agent before it acts undesirably, while allowing the agent to leverage human advice or directives to learn faster.
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability.
An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments
This paper explores the performance of fitted neural Q iteration for reinforcement learning in several partially observable environments, using three recurrent neural network architectures: Long Short-Term Memory, Gated Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of several thousands candidate architectures.
