What is Soft Actor Critic?
Soft Actor Critic (SAC) is a type of algorithm used in deep reinforcement learning (RL). It is based on the maximum entropy RL framework and is an off-policy actor-critic deep RL algorithm that combines off-policy updates with a stable stochastic actor-critic formulation. Unlike other deep RL algorithms, SAC maximizes expected reward while also maximizing entropy, meaning it acts randomly as possible to explore more widely and optimize the policy. The SAC objective ha
Stein Variational Policy Gradient (SVPG) Overview
Stein Variational Policy Gradient (SVPG) is a policy gradient-based method used in reinforcement learning to simultaneously exploit and explore multiple policies. Instead of learning a single policy, SVPG models a distribution of policy parameters.
Traditional Policy Optimization vs. SVPG
Traditional policy optimization uses a single policy for decision-making. It works by evaluating the reward or utility of different actions and then selecti
What is TayPO?
TayPO, short for Taylor Expansion Policy Optimization, is a set of algorithms used for policy optimization. The algorithms use the k-th order Taylor expansion method, which generalizes previous methods such as TRPO or trust-region policy optimization. The method unites concepts from both trust-region policy optimization and off-policy corrections.
Understanding Taylor Expansion
Taylor expansion is a mathematical method used to approximate a function $f(x)$ as a sum of terms ba
Trust Region Policy Optimization (TRPO) is a method used in reinforcement learning to update a policy gradient without changing it too much. TRPO uses a KL divergence constraint on the size of the policy update to ensure that the policy is updated within a specific range.
Off-Policy Reinforcement Learning
In off-policy reinforcement learning, the policy for collecting trajectories on rollout workers may be different from the policy that is optimized for. The objective function in an off-polic
TD3 is an advanced algorithm for reinforcement learning that builds on the DDPG algorithm. It aims to address overestimation bias with the value function, which is a common problem in reinforcement learning. The TD3 algorithm uses three key modifications: clipped double Q-learning, delayed update of target and policy networks, and target policy smoothing.
What is reinforcement learning?
Reinforcement learning is a type of machine learning that involves an agent learning to make decisions base