statisticalRL-experiments 2.2507

Experimentation backbone for the Statistical Reinforcement Learning project

Experiments

Experimentations with Learning Agents run on Statistial Reinforcement Learning environments.

Installation

pip install statisticalRL-experiments

Usage

from statisticalrl_experiments.fullExperiment import runLargeMulticoreExperiment as xp

#######################
# Import registered environments
import statisticalrl_environments.register as bW
#######################

# Instantiate one environment
env = bW.make('river-swim-6')
nS = env.observation_space.n
nA = env.action_space.n


#######################
# Import some learners
from statisticalrl_learners.Generic.Random import Random as rd
from statisticalrl_learners.Generic.Qlearning import Qlearning as ql
from statisticalrl_learners.MDPs_discrete.UCRL3 import UCRL3_lazy as ucrl3
from statisticalrl_learners.MDPs_discrete.IMED_RL import IMEDRL as imedrl
import statisticalrl_learners.MDPs_discrete.Optimal.OptimalControl  as opt

#######################
# List a few learners to be compared:
agents = []
agents.append( [rd, {"env": env}])
agents.append( [ql, {"nS":nS, "nA":nA}])
agents.append( [ucrl3, {"nS":nS, "nA":nA, "delta":0.05}])
agents.append(([imedrl, {"nbr_states":nS, "nbr_actions":nA}]))
    
#############################
# Compute oracle policy:
oracle = opt.build_opti(env.name, env, env.observation_space.n, env.action_space.n)

#######################
# Run a full experiment
# This function produces all results including plots, logs, etc in the folder "root_folder" 
#######################
xp(env, agents, oracle, timeHorizon=1000, nbReplicates=16,root_folder="results/")

#######################
# Plotting Regret directly from dump files of past runs (here until time horizon tplot=500):
#######################    
#from statisticalrl_experiments.plotResults import search_dump_cumRegretfiles, plot_results_from_dump
#files = search_dump_cumRegretfiles("RiverSwim-S6-v0", root_folder="results/")
#if files:
#    plot_results_from_dump(files, tplot=500)