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Found 8 papers, 3 papers with code
Behind the Myth of Exploration in Policy Gradients
To compute near-optimal policies, it is essential in practice to include exploration terms in the learning objective.
Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms
We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets.
Informed POMDP: Leveraging Additional Information in Model-Based RL
We then show that this informed objective consists of learning an environment model from which we can sample latent trajectories.
Policy Gradient Algorithms Implicitly Optimize by Continuation
First, we formulate direct policy optimization in the optimization by continuation framework.
Recurrent networks, hidden states and beliefs in partially observable environments
In summary, this work shows that in its hidden states, a recurrent neural network approximating the Q-function of a partially observable environment reproduces a sufficient statistic from the history that is correlated to the relevant part of the belief for taking optimal actions.
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
The results highlight the main strengths and weaknesses associated with each probability metric together with an important limitation of the Wasserstein distance.
Distributional Reinforcement Learning
reinforcement-learning
+2
Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent
We assess the performance of our algorithm in three environments concerned with the design and control of a mass-spring-damper system, a small-scale off-grid power system and a drone, respectively.
A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding
In this paper, we propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market where exchanges occur through a centralized order book.
