Examples of agents that can work with a continuous observation space, and use a value function critic, are rlACAgent, rlPGAgent, rlPPOAgent, and rlTRPOAgent. For more information on creating approximator objects such as actors and critics, see Create Policies and Value Functions. Create Actor...
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Introduced in R2022a See Also Functions getValue | getMaxQValue | getModel | evaluate | getObservationInfo | getActionInfo Objects rlNumericSpec | rlFiniteSetSpec | rlValueFunction | rlVectorQValueFunction | rlTable | rlQAgent | rlSARSAAgent | rlDQNAgent | rlDDPGAgent | rlTD3Agent Topics ...
4.2 Using Value Functions for Downstream Tasks 1.使用价值函数作为密集奖励函数 将稀疏任务奖励函数和作为势函数的值函数结合起来。 在除Freeway外的所有环境中,这方法不仅比稀疏奖励方法学习更快,加速下游任务的学习,而且可以帮助收敛到更高的平均性能。LAQ 还与offline RL 中的一些方法(如CQL和BCQ)兼容。如图中...
0x04 Value Functions in Theory 我们需要尝试解释之前取最大值的方式是否可以收敛(converge) image-20221102222722182 MDP中的bellman一定是收敛的 在利用近似网络(function approximation)的方式下并不收敛 image-20221102223053608 我们得到一个非常sad corollary,但是在下一张的DQN中可以让它变的更好 ...
4 Value function approximation (VFA) in RL The value functions for all states are stored in a designated memory in methods like MC and TD. As we know, a state is an arrangement of observation features. A feature is a unique attribute or characteristic of a phenomenon that may be measured...
Don't use a value function inthe traditional sense. Temporal difference learning of value functions in deep-RL is notoriously hard and sensitive to hyperparameters. They're also highly dependent upon the ever-changing policy. I believe there should be a more robust solution, so I set out to ...
A key component of many reinforcement learning (RL) algorithms is the approximation of the value function. The design and selection of features for approximation in RL is crucial, and an ongoing area of research. One approach to the problem of feature selection is to apply sparsity-inducing tech...
We demonstrate that value functions trained with categorical cross-entropy significantly improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-...
Dense reward functions that satisfy this condition can only improve, never worsen, sample complexity. This opens up opportunities to train efficient neural architectures with dense rewards, compounding their benefits to sample complexity. We evaluate this proposal in 12 standard benchmark environments in...