RSoccer SSL and VSSS Gym environments

RSoccer Gym is an open-source framework to study Reinforcement Learning for SSL and IEEE VSSS competition environment. The simulation is done by rSim and it is one of the requirements.

Reference

If you use this environment in your publication and want to cite it, utilize this BibTeX:

@misc{martins2021rsoccer,
      title={rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer}, 
      author={Felipe B. Martins and Mateus G. Machado and Hansenclever F. Bassani and Pedro H. M. Braga and Edna S. Barros},
      year={2021},
      eprint={2106.12895},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Requirements

Python 3.7+
OpenAI Gym
RSim >= v1.2.0
Pyglet
Protobuf

Install through The Python Package Index (PyPI)

pip install rsoccer-gym

Install through Source

git clone https://github.com/robocin/rSoccer.git
cd rSoccer
pip install .

For editable installs, change last command to "pip install -e .".

Available Envs

IEEE VSSS	IEEE VSSS Multi-Agent	GoTo Ball	Static Defenders

Contested Possession	Dribbling	Pass Endurance	Pass Endurance MA

Environment Id	Observation Space	Action Space	Step limit
VSS-v0	Box(40,)	Box(2,)	1200
VSSMA-v0	Box(3,40)	Box(3,2)	1200
VSSGk-v0	Box(40,)	Box(2,)	1200
SSLGoToBall-v0	Box(24,)	Box(3,)	2400
SSLGoToBallShoot-v0	Box(12,)	Box(5,)	1200
SSLStaticDefenders-v0	Box(24,)	Box(5,)	1000
SSLDribbling-v0	Box(21,)	Box(4,)	4800
SSLContestedPossession-v0	Box(14,)	Box(5,)	1200
SSLPassEndurance-v0	Box(18,)	Box(3,)	1200
SSLPassEnduranceMA-v0	Box(18,)	Box(2,3)	1200

Example code - Environment

import numpy as np
from gym.spaces import Box
from rsoccer_gym.Entities import Ball, Frame, Robot
from rsoccer_gym.ssl.ssl_gym_base import SSLBaseEnv


class SSLExampleEnv(SSLBaseEnv):
    def __init__(self):
        field = 0 # SSL Division A Field
        super().__init__(field_type=0, n_robots_blue=1,
                         n_robots_yellow=0, time_step=0.025)
        n_obs = 4 # Ball x,y and Robot x, y
        self.action_space = Box(low=-1, high=1, shape=(2, ))
        self.observation_space = Box(low=-self.field.length/2,\
            high=self.field.length/2,shape=(n_obs, ))

    def _frame_to_observations(self):
        ball, robot = self.frame.ball, self.frame.robots_blue[0]
        return np.array([ball.x, ball.y, robot.x, robot.y])

    def _get_commands(self, actions):
        return [Robot(yellow=False, id=0,
                      v_x=actions[0], v_y=actions[1])]

    def _calculate_reward_and_done(self):
        if self.frame.ball.x > self.field.length / 2 \
            and abs(self.frame.ball.y) < self.field.goal_width / 2:
            reward, done = 1, True
        else:
            reward, done = 0, False
        return reward, done
    
    def _get_initial_positions_frame(self):
        pos_frame: Frame = Frame()
        pos_frame.ball = Ball(x=(self.field.length/2)\
            - self.field.penalty_length, y=0.)
        pos_frame.robots_blue[0] = Robot(x=0., y=0., theta=0,)
        return pos_frame

Example code - Agent

import gym
import rsoccer_gym

# Using VSS Single Agent env
env = gym.make('VSS-v0')

env.reset()
# Run for 1 episode and print reward at the end
for i in range(1):
    done = False
    while not done:
        # Step using random actions
        action = env.action_space.sample()
        next_state, reward, done, _ = env.step(action)
        env.render()
    print(reward)

bmmuc/rSoccer