reagent.gym.envs.functionality package
Submodules
reagent.gym.envs.functionality.possible_actions_mask_tester module
Simple environment to test possible_actions_mask. State simply tells you which iteration it is, but doesn’t tell anything about which action to take, so only source of info is possible_actions_mask. The Q-value of each action to converge to the (discounted) value of the MDP.
The value of the MDP should be 10 * max_steps = 200
- class reagent.gym.envs.functionality.possible_actions_mask_tester.PossibleActionsMaskTester
Bases:
gym.core.Env- reset()
Resets the environment to an initial state and returns an initial observation.
Note that this function should not reset the environment’s random number generator(s); random variables in the environment’s state should be sampled independently between multiple calls to reset(). In other words, each call of reset() should yield an environment suitable for a new episode, independent of previous episodes.
- Returns
the initial observation.
- Return type
observation (object)
- step(action)
Run one timestep of the environment’s dynamics. When end of episode is reached, you are responsible for calling reset() to reset this environment’s state.
Accepts an action and returns a tuple (observation, reward, done, info).
- Parameters
action (object) – an action provided by the agent
- Returns
agent’s observation of the current environment reward (float) : amount of reward returned after previous action done (bool): whether the episode has ended, in which case further step() calls will return undefined results info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)
- Return type
observation (object)