Theory and Methods for Reinforcement Learning
Aperçu des semaines
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Teaching Assistants:
- Luca Viano (Head TA)
- Leello Dadi
- Pedro Abranches
- Yongtao Wu
- Zhenyu Zhu
- Andrej Janchevski
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An overview of the course
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MDPs; value and Q functions; value iteration, policy iteration; operator perspectives. Model-free policy-based and value-based methods; Monte Carlo (MC) method and temporal difference (TD) learning.
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Primal and Dual LP, ALP, ALP with constraint sampling, primal dual methods, REPS, offline LP methods.
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Policy Gradients Methods.
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Policy gradient methods II: NPG, Sample Based NPG, TRPO, exploration in policy gradients
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Exercises on Value Iteration, Policy Iteration, Modified Policy Iteration and Q Learning
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Imitation Learning: Behavioural cloning, Dagger, MCE-IRL, GAIL, P2IL, IQ-Learn
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A brief description of the project (2 pages including references) which includes the following:
the names of the project team members
motivation of the projects
formal description of the problem and the goal
references
software and computational resources you will use
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Markov Games
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Actor Critic based Deep RL: TRPO, Soft Actor Critic.
Value based Deep RL: DQN, Double DQN, Rainbow.
Robust RL and IRL.
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Dear all,
please upload for your final report by Thursday , Jun 1st at 11:59 PM.Please double-check the submission instructions that we uploaded on Moodle during the first week https://moodlearchive.epfl.ch/2022-2023/pluginfile.php/3047530/mod_resource/content/5/syllabus-2023.pdf (page 3)
In particular, we expect between 6 and 8 pages in the NeurIPS template https://neurips.cc/Conferences/2022/PaperInformation/StyleFiles
The suggested structure is
- Abstract
- Introduction
- Related Work
- Approach
- Results
- Conclusion
- References
If you ran experiments, please attach your code as supplementary material, uploading a single zip file containing the main report in pdf format and a folder named supplementary for the attached files.
It is also possible to upload an Appendix in a separate pdf including it in the same zip file.
The final class is on June 1st when you will be giving a 15 minutes presentation of your project. There is no need to submit the slides you will use at this stage.
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