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Found 18 papers, 2 papers with code
Generalised Policy Improvement with Geometric Policy Composition
We introduce a method for policy improvement that interpolates between the greedy approach of value-based reinforcement learning (RL) and the full planning approach typical of model-based RL.
Selective Credit Assignment
Efficient credit assignment is essential for reinforcement learning algorithms in both prediction and control settings.
Model-Value Inconsistency as a Signal for Epistemic Uncertainty
Unlike prior work which estimates uncertainty by training an ensemble of many models and/or value functions, this approach requires only the single model and value function which are already being learned in most model-based reinforcement learning algorithms.
Model-based Reinforcement Learning
Rolling Shutter Correction
When should agents explore?
Exploration remains a central challenge for reinforcement learning (RL).
The Option Keyboard: Combining Skills in Reinforcement Learning
Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options.
Return-based Scaling: Yet Another Normalisation Trick for Deep RL
Scaling issues are mundane yet irritating for practitioners of reinforcement learning.
Fast reinforcement learning with generalized policy updates
Both strategies considerably reduce the amount of data needed to solve a reinforcement-learning problem.
Expected Eligibility Traces
The question of how to determine which states and actions are responsible for a certain outcome is known as the credit assignment problem and remains a central research question in reinforcement learning and artificial intelligence.
Adapting Behaviour for Learning Progress
Determining what experience to generate to best facilitate learning (i. e. exploration) is one of the distinguishing features and open challenges in reinforcement learning.
Conditional Importance Sampling for Off-Policy Learning
We theoretically analyse this space, and concretely investigate several algorithms that arise from this framework.
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Rather than proposing a new method, this paper investigates an issue present in existing learning algorithms.
The Termination Critic
In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents.
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
In this paper we extend the SFs & GPI framework in two ways.
Universal Successor Features Approximators
We focus on one aspect in particular, namely the ability to generalise to unseen tasks.
Observational Learning by Reinforcement Learning
Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent.
Learning Shared Representations in Multi-task Reinforcement Learning
We investigate a paradigm in multi-task reinforcement learning (MT-RL) in which an agent is placed in an environment and needs to learn to perform a series of tasks, within this space.
The Wreath Process: A totally generative model of geometric shape based on nested symmetries
We consider the problem of modelling noisy but highly symmetric shapes that can be viewed as hierarchies of whole-part relationships in which higher level objects are composed of transformed collections of lower level objects.
