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Code for the paper Adversarial Online Multi-Task Reinforcement Learning (ALT 2023).

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ngmq/adversarial-online-multi-task-reinforcement-learning

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Paper: https://arxiv.org/abs/2301.04268

Six steps to replicate the results:

$ conda activate rlberry
  • Step 4. Train and test four agents: the optimal non-stationary agent, the AOMultiRL agent with a given distinguishing set, the one-episode UCBVI agent and the random agent.
$ python AOMultiRL1.py

At the end of this command, results for these four agents are saved in the directory Data/AOMultiRL1.

  • Step 5. Train and test the AOMultiRL2 agent that discovers a distinguishing set on its own.
$ python AOMultiRL2.py

At the end of this command, results for these four agents are saved in the directory Data/AOMultiRL2.

  • Step 6. Visualize the results by running
$ utils.py 

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Code for the paper Adversarial Online Multi-Task Reinforcement Learning (ALT 2023).

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