From 22157fc2df825915c3731680215278d2910ffbf2 Mon Sep 17 00:00:00 2001 From: angelmtenor Date: Thu, 1 Dec 2016 11:13:54 +0100 Subject: [PATCH] minor exceptions and comments --- README.md | 11 +++--- example.py | 8 ++-- rl_vrep.py | 10 +++-- rlrobot.py | 5 ++- task.py | 70 ++++++++++------------------------- vrep_scenes/C_table_cube.ttt | Bin 3124556 -> 3124555 bytes 6 files changed, 40 insertions(+), 64 deletions(-) diff --git a/README.md b/README.md index b2b7fa2..d6d8350 100644 --- a/README.md +++ b/README.md @@ -2,14 +2,15 @@ This repository provides a Reinforcement Learning framework in Python from the Machine Perception and Intelligent Robotics research group [(MAPIR)](http://mapir.isa.uma.es). ### Requirements -* Python 3 -* numpy -* matplotlib +* Python >= 3.4 +* numpy >= 1.11 +* matplotlib >= 1.5 * tkinter `sudo apt-get install python-tk` +Tested on Ubuntu 14.04 and 16.04 (64 bits). ### V-REP settings: -(Tested on V-REP_PRO_EDU_V3_3_2 64_bits Linux) +(Tested version: V-REP PRO EDU V3.3.2) 1. Use default values of `remoteApiConnections.txt` ~~~ @@ -29,6 +30,4 @@ Recommended simulation settings for scenes in RL-ROS (already set in the provide **Execute V-REP** (`./vrep.sh on linux`). `File -> Open Scene -> (open any scene for RL-ROS)` - - \ No newline at end of file diff --git a/example.py b/example.py index 0a4bc5e..ca03c60 100644 --- a/example.py +++ b/example.py @@ -10,7 +10,7 @@ import rlrobot exp.ENVIRONMENT_TYPE = "VREP" -exp.TASK_ID = "nav_1k" +exp.TASK_ID = "wander_1k" # exp.FILE_MODEL = exp.TASK_ID + "_model" # exp.N_EPISODES_MODEL = 1 @@ -18,10 +18,10 @@ exp.ACTION_STRATEGY = "QBIASR" exp.N_REPETITIONS = 1 -exp.N_EPISODES = 10 -exp.N_STEPS = 1 * 60 * 60 +exp.N_EPISODES = 1 +exp.N_STEPS = 60 * 60 -exp.SUFFIX = "Video" +exp.SUFFIX = "" exp.DISPLAY_STEP = 100 rlrobot.run() diff --git a/rl_vrep.py b/rl_vrep.py index c169742..0964eae 100755 --- a/rl_vrep.py +++ b/rl_vrep.py @@ -63,12 +63,16 @@ def show_msg(message): def connect(): """ Connect to the simulator""" + ip = '127.0.0.1' + port = 19997 vrep.simxFinish(-1) # just in case, close all opened connections global clientID - clientID = vrep.simxStart('127.0.0.1', 19997, True, True, 3000, 5) + clientID = vrep.simxStart(ip, port, True, True, 3000, 5) # Connect to V-REP - assert (clientID != -1), ("\n\n V-REP remote API server connection failed. " - "Is V-REP running?") + if clientID == -1: + import sys + sys.exit('\nV-REP remote API server connection failed (' + ip + + ':' + str(port) + '). Is V-REP running?') print('Connected to Robot') show_msg('Python: Hello') time.sleep(0.5) diff --git a/rlrobot.py b/rlrobot.py index e42c4fd..fe708ce 100755 --- a/rlrobot.py +++ b/rlrobot.py @@ -44,7 +44,10 @@ def run(): exp.check() # Check experiment parameters # copy the selected taskfile to speed up the execution: - copyfile("tasks/" + exp.TASK_ID + ".py", "task.py") + try: + copyfile("tasks/" + exp.TASK_ID + ".py", "task.py") + except IOError: + sys.exit("Task " + exp.TASK_ID + " not found. Please check exp.TASK_ID") import task import robot import lp diff --git a/task.py b/task.py index 82b02fe..5b97f7f 100644 --- a/task.py +++ b/task.py @@ -6,30 +6,28 @@ # +-----------------------------------------------+ """ Task template v1.1 """ -import math - import numpy as np import agent import robot # Task Parameters: -NAME = "navigation_2D_1024s_9a" -DESCRIPTION = "Navigation to a Goal. 16 pos / 8 orient / 8 lasers states" +NAME = "Wander_1024s_9a" +DESCRIPTION = "Wander avoiding obstacles. 5 lasers. 4 ranges each. 9 actions" ROBOT = "Pioneer 3dx with 8-point laser" -ENVIRONMENT = "VREP_SIM: square 4x4m with obstacles and a goal" +ENVIRONMENT = "VREP_SIM: square 6x6m with obstacles" ENVIRONMENT_DETAIL = "SpeedX3. Threaded Render. Sens:Buffer, Act:Streaming" AGENT_ELEMENTS = ["MOBILE_BASE", "DISTANCE_SENSOR"] -ENV_ELEMENTS = ["GOAL_OBJECT"] -# AGENT_ELEMENTS: "MOBILE_BASE","DISTANCE_SENSOR", "ARM" +ENV_ELEMENTS = [] +# AGENT_ELEMENTS: "MOBILE_BASE","DISTANCE_SENSOR","GOAL","ARM" # ENV_ELEMENTS: "GOAL_OBJECT" # Physical Parameters: STEP_TIME = 1 # s MOTOR_SPEED = 1 # rad/s (pioneer 3dx: 1 rad/s: ~ 0.1m/s) -RANGE_OBSTACLES = 0.20 # m +RANGE_OBSTACLES = 0.80 # m RANGE_DISPLACEMENT = 0.07 # m -RANGE_DAMAGE = 0.08 # m +RANGE_DAMAGE = 0.05 # m # STATES. Input variables: # Dictionary -> "variable name": (discrete intervals). Example: @@ -37,12 +35,11 @@ # "X": (0, 2, 4) 3 ranges: s(X)=0 if X<=2, =1 if X<4, =2 if X>4 # For multiple linear intervals we suggest np.linspace INPUT_VARIABLES = { - "mobile_x": np.linspace(3, 7, 4), # V-REP scenario: (3 to 7)m in X - "mobile_y": np.linspace(3, 7, 4), # V-REP scenario: (3 to 7)m in Y - "mobile_theta": np.linspace(-math.pi, math.pi, 8), - "laser_front": np.linspace(0, RANGE_OBSTACLES, 2), - "laser_front_left": np.linspace(0, RANGE_OBSTACLES, 2), - "laser_front_right": np.linspace(0, RANGE_OBSTACLES, 2) + "laser_front": np.linspace(0, RANGE_OBSTACLES, 4), + "laser_front_left": np.linspace(0, RANGE_OBSTACLES, 4), + "laser_left": np.linspace(0, RANGE_OBSTACLES, 4), + "laser_right": np.linspace(0, RANGE_OBSTACLES, 4), + "laser_front_right": np.linspace(0, RANGE_OBSTACLES, 4) } OUTPUT_VARIABLES = { "left_wheel": np.linspace(-MOTOR_SPEED, MOTOR_SPEED, 3), @@ -61,60 +58,33 @@ def execute_action(actuator): # 2 actions with only one wheel backwards changed to no motion (duplicated) if v_left < 0 and v_right < 0: v_left = v_right = MOTOR_SPEED * 2 - elif (v_left == 0 and v_right <= 0) or (v_left < 0 and v_right == 0): + elif (v_left == 0 and v_right < 0) or (v_left < 0 and v_right == 0): v_left = v_right = 0 robot.move_wheels(v_left, v_right) # left wheel, right_wheel speeds (rad/s) # TASK DEFINITION: REWARDS ---------------------------------------------------- -REWARDS = np.array([-10.0, -2.0, -1.0, -0.1, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0]) -THRES_STEPS_AT_GOAL = 5 # auxiliary: if the robot remains at the goal more than -# this value, the step time will be reduced to accelerate the experiments -steps_at_goal = 0 +REWARDS = np.array([-10.0, -2.0, -0.02, 10.0]) def get_reward(): # abstract s,a,sp pointless here """ Return the reward from s,a,sp or the environment (recommended)""" # Sensors values already updated in robot.py when the state was observed - global steps_at_goal - distance_fl = robot.sensor["laser_front_left"] distance_fr = robot.sensor["laser_front_right"] distance_f = robot.sensor["laser_front"] - distance_robot_goal = robot.distance_mobilebase_goal - displacement_robot_goal = robot.mobilebase_goal_displacement2d - # if negative: MobileBase is getting closer + displacement = robot.mobilebase_displacement2d n_collisions = (int(distance_fl < RANGE_DAMAGE) + int(distance_fr < RANGE_DAMAGE) + int(distance_f < RANGE_DAMAGE)) - - r = REWARDS[3] - - if distance_robot_goal < 0.5: # robot reaches the goal - r = max(REWARDS) - - elif n_collisions > 1: - r = min(REWARDS) # big penalty - + r = REWARDS[2] + if n_collisions > 1: # big penalty + r = min(REWARDS) elif n_collisions == 1: # small penalty r = REWARDS[1] - - elif displacement_robot_goal > RANGE_DISPLACEMENT: # moving away - r = REWARDS[2] - - elif displacement_robot_goal < -RANGE_DISPLACEMENT: # approaching - r = round(REWARDS[4] + 4 / distance_robot_goal) - r = r if r < 5 else 5 - - if r >= max(REWARDS): - steps_at_goal += 1 - if steps_at_goal > 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