ReinforcementLearningTrajectories

Design

The relationship of several concepts provided in this package:

┌───────────────────────────────────┐
│ Trajectory                        │
│ ┌───────────────────────────────┐ │
│ │ EpisodesBuffer wrapping a     | |
| | AbstractTraces                │ │
│ │             ┌───────────────┐ │ │
│ │ :trace_A => │ AbstractTrace │ │ │
│ │             └───────────────┘ │ │
│ │                               │ │
│ │             ┌───────────────┐ │ │
│ │ :trace_B => │ AbstractTrace │ │ │
│ │             └───────────────┘ │ │
│ │  ...             ...          │ │
│ └───────────────────────────────┘ │
│          ┌───────────┐            │
│          │  Sampler  │            │
│          └───────────┘            │
│         ┌────────────┐            │
│         │ Controller │            │
│         └────────────┘            │
└───────────────────────────────────┘

Trajectory

A Trajectory contains 3 parts:

• A container to store data. (Usually an AbstractTraces)
• A sampler to determine how to sample a batch from container
• A controller to decide when to sample a new batch from the container

Typical usage:

julia> t = Trajectory(Traces(a=Int[], b=Bool[]), BatchSampler(3), InsertSampleRatioControler(1.0, 3));

julia> for i in 1:5
           push!(t, (a=i, b=iseven(i)))
       end

julia> for batch in t
           println(batch)
       end
(a = [4, 5, 1], b = Bool[1, 0, 0])
(a = [3, 2, 4], b = Bool[0, 1, 1])
(a = [4, 1, 2], b = Bool[1, 0, 1])

Traces

• Traces
• MultiplexTraces
• CircularSARTTraces
• NormalizedTraces

Samplers

• BatchSampler
• MetaSampler
• MultiBatchSampler
• EpisodesSampler

Controllers

• InsertSampleRatioController
• AsyncInsertSampleRatioController

Please refer tests for common usage. (TODO: generate docs and add links to above data structures)

Acknowledgement

This async version is mainly inspired by deepmind/reverb.