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Alpha zoo train6/22/2023 ![]() ![]() There are many failure modes for this kind of learning, so it tends to be less stable. By contrast, Q-learning methods only indirectly optimize for agent performance, by training to satisfy a self-consistency equation. ![]() This tends to make them stable and reliable. The primary strength of policy optimization methods is that they are principled, in the sense that you directly optimize for the thing you want. Trade-offs Between Policy Optimization and Q-Learning. and C51, a variant that learns a distribution over return whose expectation is.DQN, a classic which substantially launched the field of deep RL,.The corresponding policy is obtained via the connection between and : the actions taken by the Q-learning agent are given by This optimization is almost always performed off-policy, which means that each update can use data collected at any point during training, regardless of how the agent was choosing to explore the environment when the data was obtained. Typically they use an objective function based on the Bellman equation. Methods in this family learn an approximator for the optimal action-value function. and PPO, whose updates indirectly maximize performance, by instead maximizing a surrogate objective function which gives a conservative estimate for how much will change as a result of the update.A2C / A3C, which performs gradient ascent to directly maximize performance,. ![]() Policy optimization also usually involves learning an approximator for the on-policy value function, which gets used in figuring out how to update the policy.Ī couple of examples of policy optimization methods are: This optimization is almost always performed on-policy, which means that each update only uses data collected while acting according to the most recent version of the policy. They optimize the parameters either directly by gradient ascent on the performance objective, or indirectly, by maximizing local approximations of. Methods in this family represent a policy explicitly as. There are two main approaches to representing and training agents with model-free RL: As of the time of writing this introduction (September 2018), model-free methods are more popular and have been more extensively developed and tested than model-based methods. While model-free methods forego the potential gains in sample efficiency from using a model, they tend to be easier to implement and tune. Model-learning is fundamentally hard, so even intense effort-being willing to throw lots of time and compute at it-can fail to pay off.Īlgorithms which use a model are called model-based methods, and those that don’t are called model-free. The biggest challenge is that bias in the model can be exploited by the agent, resulting in an agent which performs well with respect to the learned model, but behaves sub-optimally (or super terribly) in the real environment. If an agent wants to use a model in this case, it has to learn the model purely from experience, which creates several challenges. The main downside is that a ground-truth model of the environment is usually not available to the agent. When this works, it can result in a substantial improvement in sample efficiency over methods that don’t have a model. A particularly famous example of this approach is AlphaZero. Agents can then distill the results from planning ahead into a learned policy. The main upside to having a model is that it allows the agent to plan by thinking ahead, seeing what would happen for a range of possible choices, and explicitly deciding between its options. By a model of the environment, we mean a function which predicts state transitions and rewards. One of the most important branching points in an RL algorithm is the question of whether the agent has access to (or learns) a model of the environment.
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