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tanemaki / playing_with_2nd_order_markov_chain.ipynb
Last active April 19, 2024 11:55
playing_with_2nd_order_markov_chain.ipynb
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basic_rl (v.0.0.3)

A basic_rl.py provides a simple implementation of SARSA/Q-learning algorithms (specified by -a flag) with epsilon-greedy/softmax policies (specified by -p flag). You can also select the environment other than Roulette-v0 using -e flag. It also generates a graphical summary of your simulation.

Type the following command in your console to run the simulation using the default setting.

chmod +x basic_rl.py
./basic_rl.py

basic_rl (v.0.0.2)

A basic_rl.py provides a simple implementation of SARSA/Q-learning algorithms (specified by -a flag) with epsilon-greedy/softmax policies (specified by -p flag). You can also select the environment other than FrozenLake8x8-v0 using -e flag. It also generates a graphical summary of your simulation.

Type the following command in your console to run the simulation using the default setting.

chmod +x basic_rl.py

basic_rl (v0.0.1)

A basic_rl.py provides a simple implementation of SARSA/Q-learning algorithms (specified by -a flag) with epsilon-greedy/softmax policies (specified by -p flag). You can also select the environment other than FrozenLake-v0 using -e flag. It also generates a graphical summary of your simulation.

Type the following command in your console to run the simulation using the default setting.

chmod +x basic_rl.py
./basic_rl.py
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