Unverified50% confidenceFactExact time
In stochastic policy algorithms such as PPO, the policy network typically outputs a mean μ and standard deviation σ of a Gaussian distribution, and the actually executed action is sampled from N(μ, σ²); during training σ provides exploration randomness, while at inference time the mean action (σ=0) is usually taken to obtain deterministic behavior.
1
Sources
50%
Confidence
Long-term
Relevance
10/31/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedFor continuous action spaces, PPO is typically paired with a Gaussian policy output, where the network simultaneously predicts the mean and standard deviation of actions.75% similarVerifiedThe output of a decision model is often a label or probability distribution over a finite action set, similar to a policy network in reinforcement learning, whereas generative models require autoregressive sampling over a large vocabulary.72% similarUnverifiedThe traditional PPO algorithm requires simultaneously training a policy network and a value network (Critic/Value Model).72% similarVerified域随机化是指在仿真训练中刻意随机扰动物理参数，迫使策略学习到对参数变化具有泛化能力的行为72% similarUnverifiedScore-based methods directly model the score function and sample via stochastic differential equations or probability flow ODEs.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1121332API
curl https://kongchang.com/api/v1/knowledge/claims/1121332MCP
get_claim(id=1121332)