強化学習
Keras
OpenAIGym
Keras-RL
DDPG

OpenAI GymのPendulum-v0をDDPGで解く

Pendulum-v0(回転倒立振子)をKeras-RL1のDDPG2で解いてみました.

openaigym.video.0.57998.video000216.gif

問題3

状態空間

Num Observation Min Max
0 $\cos(\theta)$ (x座標) -1 1
1 $\sin(\theta)$ (y座標) -1 1
2 $\dot{\theta}$ (角速度) -8 8

行動空間

Num Action Min Max
0 $a$ (Joint effort) -2 2

報酬

$R_t = -\left ( \theta^2 + 0.1 \dot{\theta}^2 + 0.001 a^2 \right )$

DDPGとは

image.png

解法

コード

https://github.com/namakemono/openai-gym-benchmark/blob/master/envs/pendulum_v0.py

import numpy as np
import gym
from gym import wrappers
from keras.models import Model
from keras.layers import Dense, Flatten, Input, concatenate
from keras.optimizers import Adam
from rl.agents import DDPGAgent
from rl.memory import SequentialMemory

def build_actor_model(num_action, observation_shape):
    action_input = Input(shape=(1,)+observation_shape)
    x = Flatten()(action_input)
    x = Dense(16, activation="relu")(x)
    x = Dense(16, activation="relu")(x)
    x = Dense(num_action, activation="linear")(x)
    actor = Model(inputs=action_input, outputs=x)
    return actor

def build_critic_model(num_action, observation_shape):
    action_input = Input(shape=(num_action,))
    observation_input = Input(shape=(1,)+observation_shape)
    flattened_observation = Flatten()(observation_input)
    x = concatenate([action_input, flattened_observation])
    x = Dense(32, activation="relu")(x)
    x = Dense(32, activation="relu")(x)
    x = Dense(1, activation="linear")(x)
    critic = Model(inputs=[action_input, observation_input], outputs=x)
    return (critic, action_input)

def build_agent(num_action, observation_shape):
    actor = build_actor_model(num_action, observation_shape)
    critic, critic_action_input = build_critic_model(num_action, observation_shape)
    memory = SequentialMemory(limit=10**5, window_length=1)
    agent = DDPGAgent(
        num_action,
        actor,
        critic,
        critic_action_input,
        memory
    )
    return agent

def run():
    env = gym.make("Pendulum-v0")
    env = wrappers.Monitor(env, directory="/tmp/pendulum-v0", force=True)
    print("Action Space: %s" % env.action_space)
    print("Observation Space: %s" % env.observation_space)
    agent = build_agent(env.action_space.shape[0], env.observation_space.shape)
    agent.compile(Adam(lr=0.001, clipnorm=1.), metrics=["mae"])
    agent.fit(env, nb_steps=50000, visualize=True, verbose=1, nb_max_episode_steps=200)
    agent.test(env, nb_episodes=5, visualize=True, nb_max_episode_steps=200)

if __name__ == "__main__":
    run()

スコア

-382.02 ± 48.13

References