rl_agent Documentation


rl_agent is a package containing reinforcement learning (RL) agents.

rl_agent is a package containing reinforcement learning (RL) agents.

There are multiple RL agents included within this package. Q-Learning and Dyna are provided as well as a Model Based method which can accept a variety of model learning and planning methods. Two standard model-based methods that are provided are TEXPLORE and R-Max.

There are a variety of options for running the agent to select the agent type, model learning method, planner, etc:

Call agent --agent type [options]

Agent types: qlearner sarsa modelbased rmax texplore dyna savedpolicy


--seed value (integer seed for random number generator)

--gamma value (discount factor between 0 and 1)

--epsilon value (epsilon for epsilon-greedy exploration)

--alpha value (learning rate alpha)

--initialvalue value (initial q values)

--actrate value (action selection rate (Hz))

--lamba value (lamba for eligibility traces)

--m value (parameter for R-Max)

--k value (For Dyna: # of model based updates to do between each real world update)

--filename file (file to load saved policy from for savedpolicy agent)

--model type (tabular,tree,m5tree)

--planner type (vi,pi,sweeping,uct,parallel-uct,delayed-uct,delayed-parallel-uct)

--explore type (unknowns,greedy,epsilongreedy)

--combo type (average,best,separate)

--nmodels value (# of models)

--nstates value (optionally discretize domain into value # of states on each feature)

--reltrans (learn relative transitions)

--abstrans (learn absolute transitions)

--prints (turn on debug printing of actions/rewards)


ModelBasedAgent provides code for the general model based agent.

ParallelETUCT provides the code for the real-time parallel architecture.

QLearner provides the code for the Q-Learning agent.

Author(s): Todd Hester
autogenerated on Thu Jun 6 2019 22:00:14