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The pricing policy has parameters $\theta$s, and our goal is to optimized the simulation in order to produce max profits.
To do so, we need to calculate gradient of objective function(profit) w.r…
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Hi
In the text of **The advantages and disadvantages of policy-gradient methods** , you wanted to write `policy-based methods` but as a typo, you wrote `value-based methods` :
> Advantages
> Ther…
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When I try to use a 4-gpus machine to run the Analytic policy gradients training in parallel, it reports an AssertionError in `brax/training/agents/apg/train.py` line 255. Seems that it is because `t…
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I have been reading the 3 parts from "Introduction to RL" section and I have observed in part 3 that the compute_loss function for the Simplest Policy Gradient returns the mean of the product between …
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http://arxiv.org/abs/1706.05374
It is equivalent to implement a new Explorer that adds a Gaussian noise whose covariance is ρ_0 exp(cH(s)), where H(s) is a Hessian of Q(s,a) wrt a and ρ_0 and c are…
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I think one of the main motives for LoRA is to reduce memory consumption—certainly that's my motive. I'm already using gradient checkpointing and AdaFactor so the main thing I want from LoRA is to red…
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As in the title.. I spent a bit of time debugging it but haven't figured out the cause yet. E.g. running
```
tune run --nproc_per_node 2 full_finetune_distributed --config llama2/7B_full fsdp_cpu_…
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From the paper:
in fact the properform for the transition policy gradient arrived at in eqn.10.
manager_loss = -tf.reduce_sum((self.r-cutoff_vf_manager)*dcos) ( from code )
why not implement th…
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Remember that one benefit of policy gradient over Q-learning is that it can learn a stochastic policy. and then we don't have to finetune the exploration during training.
note that if we use softma…
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DQN was provided as an example. Why was the policy gradient method not provided? Does it work on these problems?