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#br >>''Common usage of running the demo programs is described here.'' #br * Build the Demo Program [#v1ba860f] Execute: $ cd benchmarks/DEMO_DIR $ make Here, DEMO_DIR is each demo directory. See [[Documentation/Installation Guide]] for the detail. * Running Command [#a9fbd19a] Execute as follows: $ ./DEMO_PRG -path PATH_LIST -agent AGENT_FILE -outdir OUT_DIR Here, the demo-specific elements are: :DEMO_PRG| Demo program whose name is *.out. :PATH_LIST| Comma separated path list for the demo program to search agent scripts. :AGENT_FILE| Comma separated agent script(s) in which a reinforcement learning module and the other modules are configured. :OUT_DIR| Result directory into which the program store some data. You need to create OUT_DIR before running; if non-existet directory is specified, no result is stored. * Result Analysis [#h9cbb14c] In OUT_DIR, the following files are stored: :cmdline| Command line of the execution. :before.agent, after.agent| A whole agent script generated by the program (before the execution and after the execution, respectively). :ext_sto| External storage directory (maybe not used in this case). :included| Directory to store the copy of every included agent file. :log-eps-ret.dat| Log file of (episode number, return in the episode). :log-action-res.dat| Log file of each action. For example, use gnuplot to plot the learning curve as: $ gnuplot gnuplot> plot 'log-eps-ret.dat' w l #ref(rl1-eps-ret.png,center,zoom,300x0) * Miscellaneous [#heb72746] In order to specify the random seed, just append an agent file as follows: $ ./maze2d.out -path ../cmn,m -agent ql_da1,seed0 -outdir result/rl1 This is a case of the Maze2D task. Here, seed0 is m/seed0.agent; in this file, the random seed is set to be zero. By specifying the random seed, we can obtain the same result in every run.
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#br >>''Common usage of running the demo programs is described here.'' #br * Build the Demo Program [#v1ba860f] Execute: $ cd benchmarks/DEMO_DIR $ make Here, DEMO_DIR is each demo directory. See [[Documentation/Installation Guide]] for the detail. * Running Command [#a9fbd19a] Execute as follows: $ ./DEMO_PRG -path PATH_LIST -agent AGENT_FILE -outdir OUT_DIR Here, the demo-specific elements are: :DEMO_PRG| Demo program whose name is *.out. :PATH_LIST| Comma separated path list for the demo program to search agent scripts. :AGENT_FILE| Comma separated agent script(s) in which a reinforcement learning module and the other modules are configured. :OUT_DIR| Result directory into which the program store some data. You need to create OUT_DIR before running; if non-existet directory is specified, no result is stored. * Result Analysis [#h9cbb14c] In OUT_DIR, the following files are stored: :cmdline| Command line of the execution. :before.agent, after.agent| A whole agent script generated by the program (before the execution and after the execution, respectively). :ext_sto| External storage directory (maybe not used in this case). :included| Directory to store the copy of every included agent file. :log-eps-ret.dat| Log file of (episode number, return in the episode). :log-action-res.dat| Log file of each action. For example, use gnuplot to plot the learning curve as: $ gnuplot gnuplot> plot 'log-eps-ret.dat' w l #ref(rl1-eps-ret.png,center,zoom,300x0) * Miscellaneous [#heb72746] In order to specify the random seed, just append an agent file as follows: $ ./maze2d.out -path ../cmn,m -agent ql_da1,seed0 -outdir result/rl1 This is a case of the Maze2D task. Here, seed0 is m/seed0.agent; in this file, the random seed is set to be zero. By specifying the random seed, we can obtain the same result in every run.
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