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Using '🎳 Environments for Reinforcement Learning' for Simulating a Monte Carlo Localization Algorithm Based on Real Data

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RoboSim VSSS and SSL gym environments

Install environments

$ pip install -e .

Available Envs

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
SSLGoToBallIR-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

import gym
import rc_gym

# Using VSS 3v3 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)

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Using '🎳 Environments for Reinforcement Learning' for Simulating a Monte Carlo Localization Algorithm Based on Real Data

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