Multi-Critic DDPG Method and Double Experience Replay
Budget: ₹12,500 – ₹37,500 INR
Abstract—The remarkable Deep Deterministic Policy Gradient
(DDPG) reinforcement learning method commonly consists of
actor learning and critic learning. The actor learning highly relies
on the critic learning, which makes the performance of DDPG
method rather sensitive to critic learning and leads to stability
issues. To further improve the stability and performance of DDPG
method, the multi-critic DDPG method (MCDDPG) is proposed
for a reliable critic learning. The average value of multiple
critics is used to replace the single critic in DDPG method for
better resistance when one critic performs badly, and multiple
independent critics can learn knowledges from environment more
widely. Besides, an extension of experience replay mechanism is
revealed for accelerating the training process. All the methods
are tested on simulated environments in OpenAI Gym platform,
and convincing experiment results should be obtained to support the
proposed methods. (we can use python instead of OpenAI)
(DDPG) reinforcement learning method commonly consists of
actor learning and critic learning. The actor learning highly relies
on the critic learning, which makes the performance of DDPG
method rather sensitive to critic learning and leads to stability
issues. To further improve the stability and performance of DDPG
method, the multi-critic DDPG method (MCDDPG) is proposed
for a reliable critic learning. The average value of multiple
critics is used to replace the single critic in DDPG method for
better resistance when one critic performs badly, and multiple
independent critics can learn knowledges from environment more
widely. Besides, an extension of experience replay mechanism is
revealed for accelerating the training process. All the methods
are tested on simulated environments in OpenAI Gym platform,
and convincing experiment results should be obtained to support the
proposed methods. (we can use python instead of OpenAI)