This repository is code written by myself as a way to gain familiarity and mastery over basic reinforcement learning algorithms. Much of the algorithms here are adapted from OpenAI's spinningup or baselines. This library will be implemented using pytorch.
LunarLanderContinuous-v2 solved was solved in ~260 episodes using DDPG.
To reproduce, execute the following
python train.py --env LunarLanderContinuous-v2 --tau 0.001.
To view pre-trained agents, execute python test.py experiments/LunarLanderContinuous-v2/luna_v2 --render or luna_v1.
luna_v2 was trained in 270 episodes and has a 100-episode average of 250 whereas luna_v1 has a 100-episode average of only 220.
Pendulum-v2 has no solved threshold, however, it clearly achieves its goal of keeping the pendulum upright when reviewed visually.
To reproduce, execute
python train.py --env Pendulum-v0.
To view pre-trained agents, execute python test.py experiments/Pendulum-v0/DDPG_1 --render or DDPG_2. Both solve the environment, though DDPG_1 was trained 'incorrectly' due to unscaled actions (ie action range was [-1,1] instead of [-2,2]).
MountainCarContinuous-v0 solved at ~1000 episodes, possibly sooner depending on seed due to need for "successful" exploration of the left slope.
To reproduce, execute python train.py --env MountainCarContinuous-v0.
To view pre-trained agents, execute python test.py experiments/MountainCarContinuous-v0/DDPG_1 --render.
BipedalWalker-v3 unsolved but gets very close to solving. Consistently hits the upper 290 points but rarely goes to 300.
To reproduce, execute python train.py --env BipedalWalker-v3 --q_lr 0.002.
To view pre-trained agent, execute python test.py -r experiments/BipedalWalker-v3/DDPG_1 --render.
Solved after 1500 episodes with a > 4800 average over 100 episodes.
To reproduce, execute python train.py --env HalfCheetah-v2.
To view pre-trained again, execute python test.py -r experiments/HalfCheetah-v2/ddpg_1 --render.
Partial solve with > 4500 average over 100 episodess (solved is > 6000 average over 100 episodes).
To reproduce, execute python train.py --env Ant-v2.
To view pre-trained again, execute python test.py -r experiments/Ant-v2/ddpg_1 --render.
python 3.6.10
gym 0.16.0
numpy 1.18.1
torch 1.3.1
CUDA Specific dependencies:
cudnn 7.6.5
cudatoolkit 10.1.243
- Implement PPO and TRPO policies
- Change
ReplayBufferto createreplay_buffer_sizelength of zero arrays as a placeholder so as to avoid out of memory errors as theReplayBufferfills up
- Add ability to resume training from pre-trained models