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Baseline PPO agent:
- Critic represents total reward
- Actor is trained to maximize critic
CBF PPO agent:
- Base critic represents nominal reward
- CBF critic represents safety reward
- Actor…
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DDPG, A2C, etc other deep reinforcement learning models (value vs policy, actor critic, critic only actor only)
Research paper will be attached below for references, 1-2 more will be a great place …
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In this example https://github.com/keras-team/keras-io/blob/master/examples/rl/actor_critic_cartpole.py, the gradient for the actor is defined as the gradient of loss $L = \sum \ln\pi (reward-value)$.…
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Our current baseline RL algorithm is DQN (more accurately it is DDQN). Named algorithm uses epsilon-greedy policies to at least have a chance of fully investigating environment in question. Using epsi…
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Here's a nice actor-critic reinforcement learning model that would be fun to re-implement in Nengo (and try different learning rules)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journ…
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# Actor-Critic Algorithms #
- Author: Vijay R. Konda, John N. Tsitsiklis
- Origin: https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf
- Related:
- PyTorch4 tutorial of: actor critic…
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Implement and explore the effectiveness of actor critic agent.
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Hello, Ben!
Thank you for a great tutorial series. I have a question regarding your [actor-critic notebook](https://github.com/bentrevett/pytorch-rl/blob/master/2%20-%20Actor%20Critic%20%5BCartPole%5…
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### Environment
OS: Windows 11
Python : CPython 3.10.14
Torchrl Version : 0.5.0
PyTorch Version : 2.4.1+cu124
Gym Environment: A custom subclass of EnvBase (from torchrl.envs)
The project I'm …