Policy-based Methods
Policy-based methods are a class of reinforcement learning algorithms that directly learn a policy, which is a mapping from states to actions, without computing a value function. These methods optimize the policy by iteratively updating the parameters of a parameterized policy, such as a neural network, using gradient ascent. Policy-based methods are particularly useful in high-dimensional or continuous action spaces, where value-based methods may struggle to converge. They can also handle stochastic policies and can learn both deterministic and stochastic policies. Policy-based methods can be further categorized into on-policy and off-policy methods, depending on whether the policy being optimized is the same as the one used to generate the data. On-policy methods, such as REINFORCE and Actor-Critic, update the policy using the current data, while off-policy methods, such as Q-learning and Deep Deterministic Policy Gradient (DDPG), use a different policy, such as an epsilon-greedy policy, to generate the data.
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