Search Results for author: Carlo D'Eramo

Found 12 papers, 6 papers with code

A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search

no code implementations11 Feb 2022 Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen

In this work, we propose two methods for improving the convergence rate and exploration based on a newly introduced backup operator and entropy regularization.

Atari Games Decision Making +1

Boosted Curriculum Reinforcement Learning

no code implementations ICLR 2022 Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

This approach, which we refer to as boosted curriculum reinforcement learning (BCRL), has the benefit of naturally increasing the representativeness of the functional space by adding a new residual each time a new task is presented.

reinforcement-learning

Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning

1 code implementation25 Mar 2021 Andrew S. Morgan, Daljeet Nandha, Georgia Chalvatzaki, Carlo D'Eramo, Aaron M. Dollar, Jan Peters

Substantial advancements to model-based reinforcement learning algorithms have been impeded by the model-bias induced by the collected data, which generally hurts performance.

Model-based Reinforcement Learning reinforcement-learning

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

1 code implementation25 Feb 2021 Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives.

reinforcement-learning

Convex Regularization in Monte-Carlo Tree Search

no code implementations1 Jul 2020 Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making.

Atari Games Decision Making

Sharing Knowledge in Multi-Task Deep Reinforcement Learning

1 code implementation ICLR 2020 Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters

We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning.

reinforcement-learning

Self-Paced Deep Reinforcement Learning

1 code implementation NeurIPS 2020 Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning.

reinforcement-learning

Deep Reinforcement Learning with Weighted Q-Learning

no code implementations20 Mar 2020 Andrea Cini, Carlo D'Eramo, Jan Peters, Cesare Alippi

In this regard, Weighted Q-Learning (WQL) effectively reduces bias and shows remarkable results in stochastic environments.

Gaussian Processes Q-Learning +2

MushroomRL: Simplifying Reinforcement Learning Research

2 code implementations4 Jan 2020 Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters

MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments.

reinforcement-learning

Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning

1 code implementation1 Jan 2020 Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function.

reinforcement-learning

Generalized Mean Estimation in Monte-Carlo Tree Search

no code implementations1 Nov 2019 Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Finally, we empirically demonstrate the effectiveness of our method in well-known MDP and POMDP benchmarks, showing significant improvement in performance and convergence speed w. r. t.

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