IEEE Robotics & Automation Magazine - June 2020 - 127

Discussion and Future Work
We presented GP-based MFRL algorithms that leverage multiple simulators with varying fidelity and costs to learn a policy in

the real world. We demonstrated empirically that the GP-based
MFRL algorithms find the optimal policies using fewer samples
than the baseline algorithms, including the original MFRL
4,200

3,800
3,600
Frozen Policy
Direct
GPQ-MFRL
Transferred

3,400
3,200
3,000
2,800

0

Average Cumulative Reward
Collected in Gazebo

Average Cumulative Reward
Collected in Gazebo

4,000

Frozen Policy
Direct
GPQ-MFRL
Transferred

3,600
3,400
3,200
0

180 360 540 720 900 1,080 1,260
Samples Collected in Gazebo
(b)

4,400

4,200
4,000

Frozen Policy
Direct
GPQ-MFRL
Transferred

3,800
3,600
3,400
0

180 360 540 720 900 1,080 1,260
Samples Collected in Gazebo
(c)

Average Cumulative Reward
Collected in Gazebo

Average Cumulative Reward
Collected in Gazebo

3,800

3,000

180 360 540 720 900 1,080 1,260
Samples Collected in Gazebo
(a)

4,400

3,200

4,000

4,200
4,000

Frozen Policy
Direct
GPQ-MFRL
Transferred

3,800
3,600
3,400
3,200

0

180 360 540 720 900 1,080 1,260
Samples Collected in Gazebo
(d)

1,400

4,400
Full GP
Sparse GP (5%)
Sparse GP (15%)
Sparse GP (25%)

1,200
1,000
800
600
400
200
0

0

600 1,200 1,800 2,400 3,000 3,600 4,200
Samples in Pygame

Figure 13. The wall clock time required to perform all GP-related
operations in Pygame for various degrees of GP sparse
approximations. The plot shows the mean time over five trials for
each case.

Average Cumulative Reward
Collected in Gazebo

Time to Perform GP Operations (s)

Figure 12. The average cumulative reward collected by the robot in the Gazebo environment as a function of the samples collected
in Gazebo for different percentages of inducing points. The plots show the averages and standard deviations of five trials for the
inducing points set to (a) 5%, (b) 15%, and (c) 25% of the training sample size as well as (d) full GP inference.

4,200
4,000
GP-VI-MFRL
GPQ-MFRL

3,800
3,600
3,400
3,200
3,000

0

180 360 540 720 900 1,080 1,260
Samples Collected in Gazebo

Figure 14. The average cumulative reward collected by the
robot in the Gazebo environment as a function of the samples
collected in Gazebo for GP-VI-MFRL and GP-Q-MFRL. The plots
show the averages and standard deviations of 10 trials.

JUNE 2020

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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127



IEEE Robotics & Automation Magazine - June 2020

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - June 2020

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