Determinism. The time determinism of execution is the foundation of all RTOS for time critical application. This means that at all time it is be possible to know what is the state of the running application (operating system + task + drivers). To achieve such a measurement, the application being benchmarked will just provide information on task scheduling. The information about which task is being executed, how long does the task has access to the scheduler and how often is scheduled the tasks. In order to have to keep measurement consistent and relevant for benchmarking, the measurements sampled would be stored in a place in the RAM for a certain period, and dump the data once the benchmarking is done or the buffer full. This will eliminate the time overhead that transferring data would add.
Determinism. A deterministic process is one that gives the same result every time some offer is made from the environment. A deterministic process does not provide any uncertainty with respect to a particular outcome, always yielding the same result. More specifically, a determin- istic process can never either diverge or have the option of performing an event or refusing it. FDR’s refinement-checking modus operandi is based on classical model-checking. This means that is performs an exhaustive exploration of all possible be- haviours of a model [CW+96]. Model-checking works by refutation; to prove ¬ a property P , a model-checker will try to find a counter-example that shows P ; if none is found, then P is deemed to be true. In the context of refinement-checking, a counter-example is a trace that highlights the non- existence of a refinement between the compared processes. The analysis presented here also used ProBE (Process Behaviour Explorer), FDR’s companion tool, now integrated within FDR3. ProBE applies the for- mal analysis technique of simulation or animation, a form of model testing that allows tests to be executed against the model. ProBE animates CSP pro- cesses allowing the user to interactively explore a process’s behaviour
Determinism. A deterministic process is one that gives the same result every time some offer is made from the environment. A deterministic process does not provide any uncertainty with respect to a particular outcome, always yielding the same result. More specifically, a determin- istic process can never either diverge or have the option of performing an event or refusing it. FDR’s refinement-checking modus operandi is based on classical model-checking. This means that is performs an exhaustive exploration of all possible be- haviours of a model [CW+96]. Model-checking works by refutation; to prove ¬
Determinism. Analyzing the paths of agents that miss their goal, there are a couple of things evident. Outer left corners corresponding to (extreme) A1 astigmatism and defocus values are difficult to deal with resulting in ‘stuck’ behavior. Secondly, the agent has significant difficulty around the goal state where real images tend to be more similar. This behavior is demonstrated in Figures 60, 61 and 68. In the DQN algorithm, when performing inference, the agent can operate in two ways. Either by taking the action adhering to the maximum Q-value as by policy default, or by treating the Q-values as probability mass function from which one draw samples. In areas where the agent is confident and robust a less optimal action may be corrected along the path. Around the goal or in a corner, it can free an agent from a zone of ambiguity. Vπ(S) = 𝔼π [ Gt ∣ St = s ],
