README.md

RL gym

Implementing different reinforcement learning algorithms on different gym environments.

These algorithms are implemented in this repo:

• A2C
• DDPG
• Double DQN
• Dueling DQN
• TD3

And tested on these environments.

Cartpole	Pendulum	Acrobat	Lunar Lander Continuous

A2C

A2C is a on-policy, model-free reinforcement learning algorithm. Here is the pseudo code for A3C which is almost similar to A2C.

Agent trained using A2C playing Acrobat game.

DDPG

DDPG is a off-policy, model-free reinforcement learning algorithm. Here is the pseudo code for DDPG

Agent trained using DDPG playing lunar lander game.

Double_DQN

Double DQN is a off-policy, model-free reinforcement learning algorithm. Here is the pseudo code for Double DQN

Agent trained using Double DQN playing Cartpole game.

Dueling_DQN

Similar to DDQN, dueling network contains two separate estimators: one for the state value function and one for the state-dependent action advantage function.

Formula for the decomposition of Q-value:

• θ is shared parameter for the network.
• α parameterizes output stream for advantage function Α.
• β parameterizes output stream for value function V.

Agent trained using Dueling DQN playing Acrobat game.

TD3

TD3 is a off-policy, model-free reinforcement learning algorithm. Here is the pseudo code for TD3

Agent trained using TD3 playing Pendulum game.

References

• Reinforcement Learning - Goal Oriented Intelligence
• Reinforcement Learning by Sentdex
• OpenAI's Spinning Up Docs
• Wang et al., Dueling Network Architectures for Deep Reinforcement Learning
• Dueling Deep Q Networks
• Deriving Policy Gradients and Implementing REINFORCE
• Understanding Actor Critic Methods and A2C
• Keras DDPG Example

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