Action noise in off-policy deep reinforcement learning: Impact on exploration and performance
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration
such as the additive action noise often used in continuous control domains. Typically, the …
such as the additive action noise often used in continuous control domains. Typically, the …
Colored Noise in PPO: Improved Exploration and Performance Through Correlated Action Sampling
Proximal Policy Optimization (PPO), a popular on-policy deep reinforcement learning
method, employs a stochastic policy for exploration. In this paper, we propose a colored …
method, employs a stochastic policy for exploration. In this paper, we propose a colored …