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RLplayground

Playing around reinforcement learning algorithms

This is a project acompanies my catching of the recent years developements in the Reinforecement learning domain. It gathers simple implementations of state-of-art RL algorithms under the same API.

Available algorithms

  • DQN: With variants (Double, Dueling PriorityMemory, NoisyNets)
  • VanilaPG
  • A2C
  • PPO: With value and norm clipping variants
  • DDPG
  • TD3
  • ICM: In progress
  • Evolution Methods: (ES CEM Etc..) Todo

Agent are sorted by the the action space types they can work with: Discrete, Continuous and Hybrid (Both)

Enviroments

CartPole

  • All discrete agents

Pendulum

  • TD3
  • PPO

LunarLander

  • DQN:
  • PPO:

LunarLanderContinuous

  • PPO: Agent can land but does'n larn to stop using throtle and get the final bonus
  • DDPG: soved in ~900 epsiodes
  • TD3:

BipedalWalker:

  • TD3:
  • PPO:

BipedalWalkerHardcore

  • TD3 fine tuned from agent trained on BipedalWalker)

AtariPong:

  • DQN:
  • PPO:

AtariBreakout

  • PPO:

Credits

Credit to all those Github repository I aspired from. I consulted a lot of repositories in order while implementing the algorithms, defining architectures and finetuning hyperparameters

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Playing around reinforcement learning algorithms

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