genrl
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SforAiDl
A PyTorch reinforcement learning library for generalizable and reproducible algorithm implementations with an aim to improve accessibility in RL
I'm trying to execute this simple code ``` import gym from genrl.agents import QLearning from genrl.trainers import ClassicalTrainer env = gym.make("FrozenLake-v0") agent = QLearning(env) trainer = ClassicalTrainer(agent, env, mode="dyna", model="tabular",...
MCTS
Progress - - [X] Added modular structure for Tree search agents and tree search planners - [X] UCT Node - [ ] OPD - [ ] OLOP - [ ]...
There's three ways that I can think of having distributed training: 1. Use of Pytorch's Distributed Training infrastructure. Would require establishing communication protocols specific to the case of Deep RL....
The current logger might go on to the next line if there are a lot of key, value pairs. There could be three solutions to this: 1. Put a limit...
Agents should be structured in a way that they can be extended to distributional or distributed agents (and both as well, case in point: D4PG and lots of others :))....
Go to the `docs/source/usage/tutorials` and add separate `.md` files to explain the following: - [x] Using A2C (@Darshan-ko ) - [ ] Using PPO1 - [x] Using VPG (@Devanshu24 )...
Save a GIF file based on this argument in trainer. To-do: 1. Check tensorboard saving in video
We should think about common loss functions that are used a lot in RL that can be packaged. As of now, we're constructing everything from scratch so we're going towards...
We should develop an environment module with wrappers. For a starter, I find [TF Agents env module](https://github.com/tensorflow/agents/tree/master/tf_agents/environments) pretty good.