How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization

3 code implementations • NeurIPS 2020 • Pierluca D'Oro, Wojciech Jaśkowski

Deterministic-policy actor-critic algorithms for continuous control improve the actor by plugging its actions into the critic and ascending the action-value gradient, which is obtained by chaining the actor's Jacobian matrix with the gradient of the critic with respect to input actions.

Continuous Control

Training Agents using Upside-Down Reinforcement Learning

7 code implementations • 5 Dec 2019 • Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, Jürgen Schmidhuber

Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments.

reinforcement-learning Reinforcement Learning (RL)

Artificial Intelligence for Prosthetics - challenge solutions

1 code implementation • 7 Feb 2019 • Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F. Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel Salathé, Sergey Levine, Scott Delp

In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.

Imitation Learning reinforcement-learning +1

Model-Based Active Exploration

2 code implementations • 29 Oct 2018 • Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez

Efficient exploration is an unsolved problem in Reinforcement Learning which is usually addressed by reactively rewarding the agent for fortuitously encountering novel situations.

Efficient Exploration Reinforcement Learning (RL)

ViZDoom Competitions: Playing Doom from Pixels

6 code implementations • 10 Sep 2018 • Marek Wydmuch, Michał Kempka, Wojciech Jaśkowski

The results of the competition lead to the conclusion that, although reinforcement learning can produce capable Doom bots, they still are not yet able to successfully compete against humans in this game.

Navigate reinforcement-learning +1

Learning to Play Othello with Deep Neural Networks

1 code implementation • 17 Nov 2017 • Paweł Liskowski, Wojciech Jaśkowski, Krzysztof Krawiec

Achieving superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural architectures (CNNs) for capturing complex spatial patterns.

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

9 code implementations • 6 May 2016 • Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, Wojciech Jaśkowski

Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world.

Atari Games Game of Doom +3

Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping

4 code implementations • 18 Apr 2016 • Wojciech Jaśkowski

With the aim to develop a strong 2048 playing program, we employ temporal difference learning with systematic n-tuple networks.

Playing the Game of 2048

Systematic N-tuple Networks for Position Evaluation: Exceeding 90% in the Othello League

no code implementations • 5 Jun 2014 • Wojciech Jaśkowski

N-tuple networks have been successfully used as position evaluation functions for board games such as Othello or Connect Four.

Board Games Position