SafetyRL_HighwayEnv

Benchmarking Safety Started RL Agents.

configurations-
Epochs: 160
Goal: Reaching the green circle, avoiding all hazards
Agent: Car

PPO

CPO

Moving to Higway Environment

Applying Constrained Policy Networks on Highway Environment.

Table of Contents

• DQN
• PPO
• CPO
• Contributors

DQN

Config-
Gamma: 0.70
Epsilon: 0.1
N_steps: 20k

Average Episode Rewards for Dqn

PPO

Config-
Gamma: 0.99
Epsilon: 0.2
N_steps: 20000

Average Episode Rewards for PPO

CPO

Config-
'discount_factor':0.8,
'hidden1':256,
'hidden2':256,
'v_lr':1e-3,
'cost_v_lr':1e-3,
'value_epochs':80,
'cost_value_epochs':80,
'num_conjugate':10,
'max_decay_num':10,
'line_decay':0.8,
'max_kl':0.01,
'max_avg_cost':800/1000,
'damping_coeff':0.01,
'gae_coeff':0.97,

How To Run

DQN:

cd scripts

python ./experiments.py evaluate ./configs/HighwayEnv/env_test.json ./configs/HighwayEnv/agents/DQNAgent/ego_attention.json --test --recover-from ./out/HighwayEnv/DQNAgent/saved_models/latest.tar

CPO:

cd SafeRL-CPO

More intructions inside the folder.

Contributors

Varun Jain
Harsh Patel
Shivam Sahni
Pushkar Mujumdar

References

1. https://github.com/eleurent/highway-env
2. https://arxiv.org/abs/1705.10528
3. https://github.com/openai/safety-starter-agents
4. https://github.com/eleurent/rl-agents
5. https://github.com/sergeivolodin/SafeContinuousStateRL