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Found 9 papers, 3 papers with code
Task Understanding from Confusing Multi-task Data
Beyond machine learning's success in the specific tasks, research for learning multiple tasks simultaneously is referred to as multi-task learning.
Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All Networks
One stream of reinforcement learning research is exploring biologically plausible models and algorithms to simulate biological intelligence and fit neuromorphic hardware.
Adjacency constraint for efficient hierarchical reinforcement learning
Searching in a large goal space poses difficulty for both high-level subgoal generation and low-level policy learning.
Subjective Learning for Open-Ended Data
In this paper, we present a novel supervised learning framework of learning from open-ended data, which is modeled as data implicitly sampled from multiple domains with the data in each domain obeying a domain-specific target function.
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations.
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a $k$-step adjacent region of the current state using an adjacency constraint.
Subjective Reinforcement Learning for Open Complex Environments
In this paper, Subjective Reinforcement Learning Framework is proposed to state the problem from a broader and systematic view, and subjective policy is proposed to represent existing related algorithms in general.
Solving single-objective tasks by preference multi-objective reinforcement learning
We analyzed the feasibility of our algorithm in theory, and further proved in experiments its better performance compared to those that design the reward function by experts.
Multi-Objective Reinforcement Learning
reinforcement-learning
Subjectivity Learning Theory towards Artificial General Intelligence
The construction of artificial general intelligence (AGI) was a long-term goal of AI research aiming to deal with the complex data in the real world and make reasonable judgments in various cases like a human.
