filler

The repository contains useful downloadable material related to my research and teaching, including Matlab software, presentations, and demonstration movies. Presentations are selectively chosen for tutorial value. If an item has a "»" button to its right, this button can be clicked to reveal more information; the "«" button then hides this information again (requires Javascript).

Software

  • Approximate RL and DP toolbox, latest snapshot, including bugfixes and new, work-in-progress algorithms and experiments - possibly with their own, new bugs. (9 January 2016, 1.9 MBytes). »
  • Approximate RL and DP toolbox, July 2013 release. (13 July 2013, 1.6 MBytes). »
  • Optimistic planning, a selection of algorithms as a stand-alone package. (13 July 2013, 79.3 KBytes). »
  • MARL toolbox documentation, the documentation files for the MARL toolbox (4 August 2010, 223.1 KBytes). »
  • MARL toolbox ver. 1.3, a Matlab multi-agent reinforcement learning toolbox (4 August 2010, 336.9 KBytes). »
  • Approximate RL and DP toolbox, developed in Matlab. (6 June 2010, 967.6 KBytes). »
  • makepdf, a Windows XP batch script to automate the creation of PDF files from DVI (21 November 2008, 2.4 KBytes).

Presentations

  • Reinforcement learning and planning algorithms, a high-level overview talk I gave at the IROS 2015 workshop on Machine Learning in Planning and Control of Robot Motion (2 October 2015, 3.0 MBytes). »
  • Nonlinear near-optimal control using optimistic planning, (Algorithms, Networked Control Systems, Real-Time Control), presented at the Italian Institute of Technology (25 September 2014, 4.6 MBytes). »
  • Optimistic planning for near-optimal control in MDPs, an in-depth description of the optimistic planning algorithm for MDPs and its analysis (1 December 2011, 1.1 MBytes). »
  • Reinforcement learning lectures, introducing classical and approximate RL (3 March 2010, 2.1 MBytes). »

Demonstration Movies

  • Assistive mobile manipulator flipping a switch, the first demo with our Cyton Gamma 1500 robot arm mounted on a Pioneer3AT mobile base. With Elod Pall and Levente Tamas. (10 November 2015).
  • Planning to swing up a rotary pendulum in real time, using the continuous-action simultaneous optimistic optimization for planning (SOOP) algorithm. With Elod Pall. (24 November 2014).
  • Final swingup solution, after the online LSPI learning experiment was completed. (8 January 2009, 864.9 KBytes).
  • Learning to swing up an inverted pendulum, using online least-squares policy iteration. (8 January 2009, 51.8 MBytes). »
  • Robot goalkeeper learning to catch the ball, using approximate online RL and experience replay (demo by Sander Adam). (1 October 2008, 13.3 MBytes).