Replay Vector

class olympus.reinforcement.replay.ReplayVector

Bases: object

Holds all the state transition of the simulation for training purposes

Notes

Steps: Number of Simulation Steps Simulation: Number of parallel simulation

Attributes:

transitions:

List of all the stored transitions

state_size:

Size of the simulation state

simulation_batch:

Number of different simulation state in one Transition Struct

grad_batch:

Total number of states in this object grad_batch = simulation_batch * len(transitions)

Methods

actions()	Returns:
next_states()	Returns:
states()	Returns:

append
critic_values
describe
entropies
log_probs
masks
rewards
to_dict

actions()

Returns:

A tensor of the action that was taken (Steps, Sim, 1)

append(transition: olympus.reinforcement.replay.Transition)

critic_values()

describe()

entropies()

grad_batch

log_probs()

masks()

next_states()

Returns:

A tensor of the simulation states (Steps, Sim, State size…)

rewards()

simulation_batch

state_size

states()

Returns:

A tensor of the simulation states (Steps, Sim, State size…)

to_dict()

transitions

class olympus.reinforcement.replay.Transition(state, action, reward, log_prob, entropy, critic, mask, next_state)

Bases: tuple

Attributes:

action

Alias for field number 1

critic

Alias for field number 5

entropy

Alias for field number 4

log_prob

Alias for field number 3

mask

Alias for field number 6

next_state

Alias for field number 7

reward

Alias for field number 2

state

Alias for field number 0

Methods

count(value, /)	Return number of occurrences of value.
index(value[, start, stop])	Return first index of value.

action

Alias for field number 1

critic

Alias for field number 5

entropy

Alias for field number 4

log_prob

Alias for field number 3

mask

Alias for field number 6

next_state

Alias for field number 7

reward

Alias for field number 2

state

Alias for field number 0