Skip to content

chenhongge/StateAdvDRL

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
DQN
 
 
PPO
 
 
 
 
 
 
 
 

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

We study the robustness of deep reinforcement learning agents when their state observations (e.g., measurements of environment states) contain noises or even adversarial perturbations. For example, a self-driving car can observe its location through GPS, however GPS signal contains uncertainty and the driving policy must take this uncertainty into consideration to plan routes safely. To guarantee performance under even the worst case uncertainty, we study the foundamental state-adversarial Markov Decision Process (SA-MDP) and propose theoretically principled robustness regularizers for PPO, DDPG and DQN. In addition, we also propose two strong adversarial attacks for PPO and DDPG, the maximal action difference (MAD) attack and the robust sarsa (RS) attack. More details can be found in our paper:

Huan Zhang*, Hongge Chen*, Chaowei Xiao, Mingyan Liu, Bo Li, Duane Boning, and Cho-Jui Hsieh, "Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations" (*Equal contribution)
NeurIPS 2020 (Spotlight)

Please also checkout our new work on optimal adversary and alternating training of learned adversary and agent (ATLA) for a robust RL agent:

Huan Zhang*, Hongge Chen*, Duane Boning, and Cho-Jui Hsieh, "Robust Reinforcement Learning on State Observations with Learned Optimal Adversary" (*Equal contribution)
ICLR 2020 (Code)

Robust Deep Reinforcement Learning Demos (SA-DQN, SA-DDPG and SA-PPO)

Pong-attack-natural.gif Pong-attack-natural.gif RoadRunner-attack-natural.gif RoadRunner-attack-natural.gif
Pong, Vanilla DQN
reward under attack: -21
(trained agent: right paddle)
Pong, SA-DQN
reward under attack: 21
(trained agent: right paddle)
RoadRunner, Vanilla DQN
reward under attack: 0
RoadRunner, SA-DQN
reward under attack: 49900
humanoid_vanilla_ppo_attack_615.gif humanoid_sappo_attack_6042.gif ant_vanilla_ddpg_attack_189.gif ant_saddpg_attack_2025.gif
Humanoid Vanilla PPO
reward under attack: 615
Humanoid SA-PPO
reward under attack: 6042
Ant Vanilla DDPG
reward under attack: 189
Ant SA-DDPG
reward under attack: 2025

Code

In our paper, we conduct experiments on deep Q network (DQN) for discrete action space environments (e.g. Atari games), deep deterministic policy gradient (DDPG) for continous action space environments with deterministic actions, and proximal policy optimization (PPO) for continous action space environments with stochastic actions. Our proposed algorithms (SA-PPO, SA-DDPG and SA-PPO) are evaluated using 11 environments.

Reference implementation for SA-DQN can be found at https://github.com/chenhongge/SA_DQN.

Reference implementation for SA-PPO can be found at https://github.com/huanzhang12/SA_PPO.

Reference implementation for SA-DDPG can be found at https://github.com/huanzhang12/SA_DDPG.

Pretrained agents performance

Pretrained DQN agents

Environment Evaluation Vanilla DQN SA-DQN (convex relaxation)
Pong No attack 21.0±0.0 21.0±0.0
PGD 10-step attack -21.0±0.0 21.0±0.0
PGD 50-step attack -21.0±0.0 21.0±0.0
RoadRunner No attack 45534.0±7066.0 44638.0±7367.0
PGD 10-step attack 0.0±0.0 44732.0±8059.5
PGD 50-step attack 0.0±0.0 44678.0±6954.0
BankHeist No attack 1308.4±24.1 1235.4±9.8
PGD 10-step attack 56.4±21.2 1232.4±16.2
PGD 50-step attack 31.0±32.6 1234.6±16.6
Freeway No attack 34.0±0.2 30.0±0.0
PGD 10-step attack 0.0±0.0 30.0±0.0
PGD 50-step attack 0.0±0.0 30.0±0.0

See our SA-DQN repository for more details.

Pretrained PPO agents

We repeatedly train each agent configuration at least 15 times, and rank them with their average cumulative rewards over 50 episodes under the strongest attack (among 5 attacks used). We report the performance for agents with median robustnes (we do not cherry-pick the best agents).

Environment Evaluation Vanilla PPO SA-PPO (convex) SA-PPO (SGLD)
Humanoid-v2 No attack 5270.6 6400.6 6624.0
Strongest attack 884.1 4690.3 6073.8
Walker2d-v2 No attack 4619.5 4486.6 4911.8
Strongest attack 913.7 2076.1 2468.4
Hopper-v2 No attack 3167.6 3704.1 3523.1
Strongest attack 733 1224.2 1403.3

See our SA-PPO repository for more details.

Pretrained DDPG agents

We attack each agent with 5 different attacks (random attack, critic attack, MAD attack, RS attack and RS+MAD attack). Here we report the lowest reward of all 5 attacks in "Strongest attack" rows. Additionally, we train each setting 11 times and we report the agent with median robustness (we do not cherry-pick the best results). This is important due to the potential large training variance in RL.

Environment Evaluation Vanilla DDPG SA-DDPG (SGLD) SA-DDPG (convex)
Ant-v2 No attack 1487 2186 2254
Strongest attack 142 2007 1820
Walker2d-v2 No attack 1870 3318 4540
Strongest attack 790 1210 1986
Hopper-v2 No attack 3302 3068 3128
Strongest attack 606 1609 1202
Reacher-v2 No attack -4.37 -5.00 -5.24
Strongest attack -27.87 -12.10 -12.44
InvertedPendulum-v2 No attack 1000 1000 1000
Strongest attack 92 423 1000

See our SA-DDPG repository for more details.

About

[NeurIPS 2020, Spotlight] Code for "Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations"

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published