rl_lib's Introduction

RlLib

RlLib is a library for reinforcement learning using Axon. It is based on libraries such as chainerrl and pfrl.

Please note that the API is subject to change as it is under development.

Supported Agents: DQN

Supported Explorers: epsilon_greedy

Currently, only DQN is supported. The goal is to increase the number of supported agents in the future.

Installation

RlLib is not yet registered on HexDocs, please use it from Git.

def deps do
  [
    {:rl_lib, git: "[email protected]:masahiro-999/rl_lib.git"},
  ]
end

##　How to use

Still in progress.

Please refer to the CartPole sample for reference.

https://github.com/masahiro-999/cart_pole_rl

defmodule CartPoleRl do

  alias RlLib.Agents.DQN
  alias RlLib.QNet

  def run() do
    q_function =
      Axon.input("x", shape: {nil, 4})
      |> Axon.dense(128, activation: :relu)
      |> Axon.dense(128, activation: :relu)
      |> Axon.dense(2)

    env = EnvGym.new("CartPole-v1", render_mode: "human")

    explorer =
      RlLib.Explorers.EpsilonGreedyExponentialDecay.new(0.3, 0.3, 200, 2)

    agent =
      DQN.new(
        q_function: q_function,
        optimizer: Axon.Optimizers.adam(0.001),
        explorer: explorer,
        target_update_interval: 20,
        buffer_size: 10000,
        gamma: 0.9,
        batch_size: 100
      )

    agent = RlLib.Experiments.train_agent(agent, env, 300, "cart-pole-qnet-300",
      step_hooks: {&step_hooks/2, nil}
    )
    IO.puts("----eval----")
    RlLib.Experiments.eval_agent(agent, env, 20,
      step_hooks: {&step_hooks/2, nil}
    )

  end

  def step_hooks(nil, experiments) do
    episode_count = experiments.episode_count
    total_reward = experiments.total_reward
    agent = experiments.agent
    IO.puts("#{episode_count}, #{total_reward} #{inspect(QNet.get_info(agent.q_net))}")

    nil
  end
end

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