This Repository is Reinforcece Learning Implementation related with PPO. The framework used in this Repository is Pytorch. The multi-processing method is basically built in. The agents are trained by PAAC(Parallel Advantage Actor Critic) strategy.

• Script : LunarLander_ppo.py
• Environment : LunarLander-v2
• Orange : 8 Process, Blue : 4 Process, Red : 1 Process

• Script : Breakout_ppo.py
• Environment : BreakoutDeterministic-v4
• Red: 8 Process, Blue: 4 Process, Orange: 1 Process

• Script : Breakout_ppo_icm.py
• Environment : BreakoutNoFrameskip-v4(handled by custom environment)
• With no environment Reward
• Because the game initial key is not selected, the peak point and performance drop is generated.
• Left : Comparison between (extrinsic reward and intrinsic, oragne) and (only intrinsic reward, gray), the average of three times of experiment
• Right : only intrinsic reward
• 32 process

• Script : MountainCar_ppo_icm.py
• Environment : MountainCart-v0
• With no environment Reward
• 32 process

• Script : PushBlock_ppo_icm.py
• Environment : PushBlock
• 32 Environment, PAAC
• orange : 0.5int + 0.5ext, blue : only int, Red : only ext
• reward shaping for sparse-reward environment : sucess - 1, others - 0
• The environment has not sparsed-reward property even if the reward is engineered to two categories(0, 1)

• Script : Pyramid_ppo_icm.py
• Environment : Pyramid
• 16 Environment, PAAC
• orange : only ext, blue : 0.01int + 0.99ext

[1] mario_rl

[2] Proximal Policy Optimization

[2] Efficient Parallel Methods for Deep Reinforcement Learning

[3] High-Dimensional Continuous Control Using Generalized Advantage Estimation

[4] Curiosity-driven Exploration by Self-supervised Prediction

[5] Large-Scale Study of Curiosity-Driven Learning

[6] curiosity-driven-exploration-pytorch

[7] ml-agents

[8] Unity: A General Platform for Intelligent Agents

[9] Solving sparse-reward tasks with Curiosity