dacbench.agents
Submodules
Package Contents
Classes
Abstract class to implement for use with the runner function |
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Abstract class to implement for use with the runner function |
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Abstract class to implement for use with the runner function |
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Abstract class to implement for use with the runner function |
- class dacbench.agents.GenericAgent(env, policy)
Bases:
dacbench.abstract_agent.AbstractDACBenchAgentAbstract class to implement for use with the runner function
- act(self, state, reward)
Compute and return environment action
- Parameters
state – Environment state
reward – Environment reward
- Returns
Action to take
- Return type
action
- train(self, next_state, reward)
Train during episode if needed (pass if not)
- Parameters
next_state – Environment state after step
reward – Environment reward
- end_episode(self, state, reward)
End of episode training if needed (pass if not)
- Parameters
state – Environment state
reward – Environment reward
- class dacbench.agents.RandomAgent(env)
Bases:
dacbench.abstract_agent.AbstractDACBenchAgentAbstract class to implement for use with the runner function
- act(self, state, reward)
Compute and return environment action
- Parameters
state – Environment state
reward – Environment reward
- Returns
Action to take
- Return type
action
- train(self, next_state, reward)
Train during episode if needed (pass if not)
- Parameters
next_state – Environment state after step
reward – Environment reward
- end_episode(self, state, reward)
End of episode training if needed (pass if not)
- Parameters
state – Environment state
reward – Environment reward
- class dacbench.agents.StaticAgent(env, action)
Bases:
dacbench.abstract_agent.AbstractDACBenchAgentAbstract class to implement for use with the runner function
- act(self, state, reward)
Compute and return environment action
- Parameters
state – Environment state
reward – Environment reward
- Returns
Action to take
- Return type
action
- train(self, next_state, reward)
Train during episode if needed (pass if not)
- Parameters
next_state – Environment state after step
reward – Environment reward
- end_episode(self, state, reward)
End of episode training if needed (pass if not)
- Parameters
state – Environment state
reward – Environment reward
- class dacbench.agents.DynamicRandomAgent(env, switching_interval)
Bases:
dacbench.abstract_agent.AbstractDACBenchAgentAbstract class to implement for use with the runner function
- act(self, state, reward)
Compute and return environment action
- Parameters
state – Environment state
reward – Environment reward
- Returns
Action to take
- Return type
action
- train(self, next_state, reward)
Train during episode if needed (pass if not)
- Parameters
next_state – Environment state after step
reward – Environment reward
- end_episode(self, state, reward)
End of episode training if needed (pass if not)
- Parameters
state – Environment state
reward – Environment reward