Advantage Actor-Critic (A2C) reinforcement learning agent used to control the motor speeds on a quadcopter in order to keep the quadcopter in a stable hover following a random angular acceleration perturbation between 0-3 degrees per second in each of the control axes: pitch, roll, and yaw. ...
deep-neural-networksreinforcement-learningdeep-learningdeep-reinforcement-learningrainbowrlcodebasedeep-q-networksacdeep-q-learningmujocomodel-freeoff-policydm-controlsoft-actor-critic UpdatedMar 21, 2021 Python JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Rei...
python reinforcement-learning deep-learning berkeley deep-reinforcement-learning openai-gym pytorch neural-networks policy-gradient deep-q-learning mujoco model-based-rl actor-critic-algorithm model-free-rl Updated Nov 21, 2022 Python garlicdevs / Fruit-API Star 70 Code Issues Pull requests A ...
Master Thesis Project: Social Learning in Multi-Agent Reinforcement Learning for Carbon Emission Reduction reinforcement-learningrenewable-energysocial-learningdemand-managementsoft-actor-critic UpdatedDec 1, 2023 Shell Add a description, image, and links to thesoft-actor-critictopic page so that developers...
Advanced-Soft-Actor-Critic This project is the algorithm Soft Actor-Critic with a series of advanced features implemented by PyTorch. It can be used to train Gym, PyBullet and Unity environments with ML-Agents. Features N-step V-trace (IMPALA: Scalable Distributed Deep-RL with Importance Weighte...
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policy_net(features) def forward_critic(self, features: th.Tensor) -> th.Tensor: return self.value_net(features) class CustomActorCriticPolicy(ActorCriticPolicy): def __init__( self, observation_space: spaces.Space, action_space: spaces.Space, lr_schedule: Callable[[float], float], *args...
'critic_optimizer_state_dict':self.critic_optim.state_dict(), 'policy_optimizer_state_dict':self.policy_optim.state_dict()},ckpt_path) # Load model parameters defload_checkpoint(self,ckpt_path,evaluate=False): print('Loading models from {}'.format(ckpt_path)) ...
Breadcrumbs SmartST / Model_actor_critic.pyTop File metadata and controls Code Blame 290 lines (237 loc) · 12 KB Raw from __future__ import print_function import paddle import paddle.fluid as fluid import numpy as np import sys def conv_bn_layer(main_input, ch_out, filter_size, stri...
target_actor = beta*actor + (1-beta)*target_actor target_critic = beta*critic + (1-beta)*target_critic where beta = 0.001 Performance of DDPG on OpenAI Envs Pendulum-v0 Below is the performance of the model after 70 episodes. Full Video BiPedalWalker-v2 Below is the performance of th...