Search Results for author:
Found 17 papers, 3 papers with code
Batch Reinforcement Learning with Hyperparameter Gradients
We consider the batch reinforcement learning problem where the agent needs to learn only from a fixed batch of data, without further interaction with the environment.
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
This paves the way for new research directions, e. g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.
Model-based Reinforcement Learning
reinforcement-learning
+1
Compatible features for Monotonic Policy Improvement
In this work we establish conditions under which the parametric approximation of the critic does not introduce bias to the updates of surrogate objective.
Policy Optimization Through Approximate Importance Sampling
These proxy objectives allow stable and low variance policy learning, but require small policy updates to ensure that the proxy objective remains an accurate approximation of the target policy value.
Disentangled Skill Embeddings for Reinforcement Learning
We propose a novel framework for multi-task reinforcement learning (MTRL).
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Soft Q-Learning with Mutual-Information Regularization
We show that the prior optimization introduces a mutual-information regularizer in the RL objective.
Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks
This paper proposes a new optimization objective for value-based deep reinforcement learning.
Learning High-level Representations from Demonstrations
A common approach to HL, is to provide the agent with a number of high-level skills that solve small parts of the overall problem.
Learning with Options that Terminate Off-Policy
Generally, learning with longer options (like learning with multi-step returns) is known to be more efficient.
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.
Forecasting day-ahead electricity prices in Europe: the importance of considering market integration
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance.
Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning
This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment.
Analysing Congestion Problems in Multi-agent Reinforcement Learning
Congestion problems are omnipresent in today's complex networks and represent a challenge in many research domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control
Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with 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.
Off-Policy Reward Shaping with Ensembles
While PBRS is proven to always preserve optimal policies, its effect on learning speed is determined by the quality of its potential function, which, in turn, depends on both the underlying heuristic and the scale.
Off-Policy Shaping Ensembles in Reinforcement Learning
Recent advances of gradient temporal-difference methods allow to learn off-policy multiple value functions in parallel with- out sacrificing convergence guarantees or computational efficiency.
